How do I ensure the candidate data is up to date?

The Firefish MCP server fetches data directly from your live Firefish environment. Use the 'getCandidate' tool to retrieve the most recent profile information for any specific candidate ID.

Can I filter jobs by their current status?

Yes! The 'listJobs' tool retrieves all active jobs. You can then ask your AI agent to sort or filter these results based on status, title, or hiring company.

What format should I use for creating a new candidate?

Use the 'createCandidate' tool by providing the first name, last name, and email address. The agent will handle the underlying API request to ensure the record is created correctly in Firefish.

Firefish MCP Server for LlamaIndex 12 tools — connect in under 2 minutes

Name: Firefish
Availability: InStock
Author: Vinkius

docs.llamaindex.ai/en/stable/module_guides/tools/mcp

Built by Vinkius GDPR 12 Tools Framework

LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Firefish as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

python

import asyncio
from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

async def main():
    # Your Vinkius token. get it at cloud.vinkius.com
    mcp_client = BasicMCPClient("https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
    mcp_tool_spec = McpToolSpec(client=mcp_client)
    tools = await mcp_tool_spec.to_tool_list_async()

    agent = FunctionAgent(
        tools=tools,
        llm=OpenAI(model="gpt-4o"),
        system_prompt=(
            "You are an assistant with access to Firefish. "
            "You have 12 tools available."
        ),
    )

    response = await agent.run(
        "What tools are available in Firefish?"
    )
    print(response)

asyncio.run(main())

Fully ManagedVinkius Servers

60%Token savings

High SecurityEnterprise-grade

IAMAccess control

EU AI ActCompliant

DLPData protection

V8 IsolateSandboxed

Ed25519Audit chain

<40msKill switch

Stream every event to Splunk, Datadog, or your own webhook in real-time

* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure

About Firefish MCP Server

Connect your Firefish account to any AI agent and automate your recruitment workflows through the Model Context Protocol (MCP). Firefish is a high-performance recruitment CRM that empowers agencies to reach more candidates and close more placements. Now, you can interact with your recruitment data directly through natural conversation.

LlamaIndex agents combine Firefish tool responses with indexed documents for comprehensive, grounded answers. Connect 12 tools through Vinkius and query live data alongside vector stores and SQL databases in a single turn. ideal for hybrid search, data enrichment, and analytical workflows.

What you can do

Candidate Management — List all candidates, fetch detailed profiles, and create new candidate records instantly.
Job Tracking — Monitor active job vacancies and retrieve complete metadata for any job in your system.
Company & Contact Insights — Access your database of client companies and contacts to stay informed before meetings or calls.
Placement Monitoring — Keep track of successful job placements and recruitment progress across your team.
Advertising Overview — List active job advertisements to see where your recruitment efforts are focused.
Activity Actions — Retrieve a list of recent recruiter actions to maintain a clear audit trail of engagement.
Seamless Integration — Securely connect your Firefish environment using your Client ID and Secret for an automated experience.

The Firefish MCP Server exposes 12 tools through the Vinkius. Connect it to LlamaIndex in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

How to Connect Firefish to LlamaIndex via MCP

Follow these steps to integrate the Firefish MCP Server with LlamaIndex.

Install dependencies

Run pip install llama-index-tools-mcp llama-index-llms-openai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Run the agent

Save to agent.py and run: python agent.py

Explore tools

The agent discovers 12 tools from Firefish

Why Use LlamaIndex with the Firefish MCP Server

LlamaIndex provides unique advantages when paired with Firefish through the Model Context Protocol.

Data-first architecture: LlamaIndex agents combine Firefish tool responses with indexed documents for comprehensive, grounded answers

Query pipeline framework lets you chain Firefish tool calls with transformations, filters, and re-rankers in a typed pipeline

Multi-source reasoning: agents can query Firefish, a vector store, and a SQL database in a single turn and synthesize results

Observability integrations show exactly what Firefish tools were called, what data was returned, and how it influenced the final answer

Firefish + LlamaIndex Use Cases

Practical scenarios where LlamaIndex combined with the Firefish MCP Server delivers measurable value.

Hybrid search: combine Firefish real-time data with embedded document indexes for answers that are both current and comprehensive

Data enrichment: query Firefish to augment indexed data with live information before generating user-facing responses

Knowledge base agents: build agents that maintain and update knowledge bases by periodically querying Firefish for fresh data

Analytical workflows: chain Firefish queries with LlamaIndex's data connectors to build multi-source analytical reports

Firefish MCP Tools for LlamaIndex (12)

These 12 tools become available when you connect Firefish to LlamaIndex via MCP:

create_candidate

Create a new candidate

get_candidate

Get candidate details

get_company

Get company details

get_contact

Get contact details

get_job

Get job details

list_actions

List actions

list_adverts

List job adverts

list_candidates

List candidates

list_companies

List companies

list_contacts

List contacts

list_jobs

List jobs

list_placements

List placements

Example Prompts for Firefish in LlamaIndex

Ready-to-use prompts you can give your LlamaIndex agent to start working with Firefish immediately.

"List all active job vacancies at Firefish."

"Search for a candidate named 'John Smith'."

"Show me the most recent recruiter actions."

Troubleshooting Firefish MCP Server with LlamaIndex

Common issues when connecting Firefish to LlamaIndex through the Vinkius, and how to resolve them.

BasicMCPClient not found

Install: pip install llama-index-tools-mcp

Firefish + LlamaIndex FAQ

Common questions about integrating Firefish MCP Server with LlamaIndex.

How does LlamaIndex connect to MCP servers?

Use the MCP client adapter to create a connection. LlamaIndex discovers all tools and wraps them as query engine tools compatible with any LlamaIndex agent.

Can I combine MCP tools with vector stores?

Yes. LlamaIndex agents can query Firefish tools and vector store indexes in the same turn, combining real-time and embedded data for grounded responses.