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mcp-intelligence-context

mcp-intelligence-context

by LeoChimal09
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
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get_relevant_context

Retrieve a token-budgeted context package with symbols and code excerpts for files most relevant to your query, avoiding full-file scanning.

Instructions

Build a focused, token-budgeted context package (symbol tables + small code excerpts) for the files most relevant to a query. This is the main entry point for "help me find/fix X" style requests — use it instead of reading whole files or globbing the repo.

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": {
      "query": "<query>"
    },
    "name": "get_relevant_context"
  }
}

Input Schema

NameRequiredDescriptionDefault
queryYes
repo_rootNo
token_budgetNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.5/5.0
Behavior3/5

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

With no annotations, the description risks the full burden of behavioral disclosure. It communicates that the tool returns a synthesized context package rather than raw files, and that it is token-limited, but it does not state whether it is strictly read-only, whether it depends on an existing repository index, or what the actual response format looks like.

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 concise and well-structured. The first sentence states the core action and output, and the second provides routing guidance. There is no filler or repetition.

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

Completeness3/5

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

The description gives enough to orient an agent to the main purpose and broad usage, but it leaves gaps around the return structure, required prior state (e.g., indexation), and parameter behaviors. Because there is no output schema, more detail about the package shape would help.

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

Parameters2/5

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

Schema description coverage is 0%, and the description does not explain the three parameters. 'query' and 'token-budgeted' are lightly implied, but repo_root is entirely undocumented, meaning an agent cannot infer the expected input format or behavior for that parameter without external knowledge.

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

Purpose4/5

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

The description clearly states the tool builds a focused, token-budgeted context package of symbol tables and code excerpts for relevant files. It distinguishes itself as the 'main entry point' for find/fix requests, which differentiates it from lower-level or broader repository tools, though it does not explicitly name all sibling tools.

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?

It explicitly says to use this tool for 'help me find/fix X' requests and to use it instead of reading whole files or globbing the repository. This provides a clear when and when-not, but it does not name specific alternative tools such as search_code or get_file_summary.

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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