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Scholar Library

Scholar Library

by SAKURAfan1023
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Python
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index_semantic_search

Indexes up to 32 source excerpts through a configured embedding service, storing vectors locally for semantic search that resumes and rebuilds when the model changes.

Instructions

将最多32个原文片段发送到已配置的embedding服务并本地保存向量；重调续接，模型改变后使用新索引。

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": {
      "project_id": "<project_id>"
    },
    "name": "index_semantic_search"
  }
}

Input Schema

NameRequiredDescriptionDefault
project_idYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

B3.4/5.0
Behavior4/5

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

Beyond the annotations (readOnly=false, openWorld=true, destructive=false), it discloses the batch cap of 32 chunks, local vector persistence, resumption of interrupted runs, and the model-change/new-index rule. It still omits cost, auth, and what happens to a pre-existing index, so not a 5.

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?

A compact, front-loaded sentence that leads with the core action and appends the resumption and model-change caveats. Dense but every clause carries operational meaning.

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?

For an indexing tool with no output schema and a single parameter, the description covers batching, persistence, resumption, and re-indexing triggers. It leaves the project_id parameter and the fate of an existing index unexplained, so it is adequate but incomplete.

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?

There is one parameter (project_id) with 0% schema description coverage, and the description says nothing about it. With low coverage the description is expected to compensate, and it does not, though the parameter name is largely self-explanatory.

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 names a concrete action (send up to 32 source chunks to the configured embedding service and store vectors locally), making the indexing purpose clear. It does not explicitly contrast with siblings like search_literature or search_library, so it stops short of a 5.

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?

It gives two usage conditions: re-invocation resumes an interrupted job, and a new index should be used after the model changes. However, it names no alternative tools and gives no when-not guidance, so usage is only implied.

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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