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Code-Fundi MCP Server

Code-Fundi MCP Server

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by Code-Fundi
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TypeScript
Remote

code-fundi-chat

Read-only

Chat with Code-Fundi AI to get code-aware answers, including conversation threading, repository knowledge, embeddings memory, and voice mode.

Instructions

Send a message to Code-Fundi AI (Fundi chat: POST /v1/fundi/chat). Supports threading, optional code context, indexed repository knowledge (knowledge_id), embeddings memory, and voice mode. Responses are streamed by the API and returned as plain text (or JSON when the server uses JSON mode).

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "prompt": "<prompt>"
    },
    "name": "code-fundi-chat"
  }
}

Input Schema

NameRequiredDescriptionDefault
embedNoEnable conversation memory via embeddings (default false)
modelNoAI model ID to use
voiceNoRequest voice/audio path on the server (default false)
promptYesUser message (sent to the API as `question`)
contextNoPrevious conversation messages for context
code_blockNoOptional code snippet combined with the question for code-aware answers
conversationNoConversation ID for threading (continues a previous conversation)
knowledge_idNoRepository / data-source UUIDs to pull indexed knowledge context into the chat (API field `knowledge`)

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.4

TDQS

A4.2/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

The description goes beyond the annotations by disclosing that responses are streamed and returned as plain text (or JSON), and it highlights features like embeddings memory and voice mode. It does not contradict the readOnlyHint and openWorldHint annotations, and adds useful behavioral context without needing to cover every safety detail.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is three concise sentences, front-loaded with the core function, followed by key capabilities and output format. Every sentence contributes meaning without redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Despite having 8 parameters and no output schema, the description adequately covers the output (streaming text/JSON) and mentions major features. It lacks explicit details on error handling or rate limits, but given the full schema coverage and available annotations, it is largely complete for a chat tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema already provides descriptions for all 8 parameters (100% coverage), so the description does not need to compensate. It does add high-level context for knowledge_id and embed, but this does not go beyond what the schema already conveys, hence a baseline score of 3.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states 'Send a message to Code-Fundi AI' with a specific verb and resource. It also lists distinguishing features like threading, code context, and knowledge integration, which sets it apart from sibling tools such as code-fundi-conversation and code-fundi-search.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description conveys when to use the tool by enumerating supported capabilities (threading, code context, knowledge, memory, voice). However, it does not explicitly mention when not to use it or name alternative sibling tools, leaving some room for interpretation.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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