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Senior Code MCP

Senior Code MCP

by Tamaz-sujashvili
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
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ingest_repo

Parse, chunk, and embed repository files into Qdrant, then build a structural graph for semantic search, giving coding agents searchable memory.

Instructions

Ingest a repo: parse, chunk, embed + upsert to Qdrant, build + save graph.

Appends to the existing collection by default so multiple repos coexist; pass reset=True to drop + recreate the collection first. Every chunk is tagged with its repo name.

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": {
      "path": "<path>"
    },
    "name": "ingest_repo"
  }
}

Input Schema

NameRequiredDescriptionDefault
pathYes
resetNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.4/5.0
Behavior4/5

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

With no annotations available, the description carries the full burden and does well: it discloses the side effects (appends to existing collection, reset=True drops and recreates), and adds context about chunk tagging. It does not mention error handling or return values, but the key behavioral traits are transparent.

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 front-loaded with the core action. Each sentence earns its place: the first summarizes the pipeline, the second details the append/reset behavior and the repo tagging. No redundant or vague wording.

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?

Given the tool's complexity (parsing, chunking, embedding, upserting, graph building) and lack of output schema, the description covers the main steps and side effects but omits details like return values, error cases, or idempotency across repeated ingests. Overall, it provides a solid but not exhaustive picture.

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

Parameters4/5

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

Schema description coverage is 0%, so the description must compensate. It explicitly explains the 'reset' parameter (drop + recreate collection first) and implies 'path' by describing the tool as 'ingest a repo'. While path is not explicitly described, the intent is clear enough; the description adds meaningful semantics beyond the bare schema.

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 uses a specific verb ('Ingest') with a clear resource ('repo') and enumerates the exact steps: parse, chunk, embed + upsert to Qdrant, build + save graph. This clearly distinguishes it from sibling search tools like search_context or search_similar_code, which are query-oriented.

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 implies usage for adding a repo to the knowledge base, contrasting with the search-focused siblings. It explains the default append behavior and the reset option, providing clear context. However, it does not explicitly state 'when not to use' or name alternatives, so it misses the explicit exclusion guidance.

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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