profile-project

by goosefly99

Overview Schema Source Related Servers Discussions Admin

Python

Local

pp_query

Search a profiled project's vectorstore to retrieve ranked, attributed text segments matching your query.

Instructions

Embed query -> vector search -> ranked, attributed hits (§11).

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": "pp_query"
  }
}

Input Schema

Name	Required	Description	Default
`query`	Yes
`top_k`	No
`where`	No

Output Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.3Jul 29, 2026

TDQS

B3.2/5.0

Behavior3/5

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

With no annotations, the description carries full burden. It outlines the core behavioral flow (embed, search, return hits) but omits details like whether the tool is read-only, destructive, or requires authentication. The description is adequate for basic understanding but lacks depth.

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

Conciseness4/5

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

The description is a single, compact sentence that conveys the core pipeline. However, the inclusion of '§11' may confuse agents without context, slightly reducing clarity.

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

Completeness2/5

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

Given the tool's complexity (3 parameters, output schema), the description is woefully incomplete. It does not explain the search behavior, result format, or parameter defaults/options. An output schema exists but is not referenced, leaving the agent under-informed.

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

Parameters1/5

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

Schema description coverage is 0%, and the description provides no information about the parameters (query, top_k, where). The agent cannot understand parameter roles, constraints, or usage from the description alone, making it nearly useless for correct invocation.

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 the tool performs an embedding query followed by vector search, returning ranked and attributed hits. It distinguishes itself from sibling tools (e.g., config, pipeline phases, index building) by focusing on querying a vector store, making its purpose specific and unambiguous.

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

Usage Guidelines3/5

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

The description implies usage for vector search queries but provides no explicit guidance on when to use this tool versus siblings, nor does it mention prerequisites or alternatives. The reference to §11 is obscure and not helpful for an AI agent.

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