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solograph

solograph

by fortunto2
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Rust
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session_search

Search past Claude Code chat sessions by topic or project to find how you solved a problem or when you worked on a specific feature.

Instructions

Semantic search over Claude Code chat session history.

Finds past sessions by what was discussed or worked on. Useful for "how did I solve X?" or "when did I work on Y?" questions.

Args: query: Search query (e.g. "knowledge graph implementation", "OCR receipt scanning") n_results: Number of results (default 5) project: Filter by project name (e.g. "my-app", "backend")

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": "session_search"
  }
}

Input Schema

NameRequiredDescriptionDefault
queryYes
projectNo
n_resultsNo

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.6.0

TDQS

A4.1/5.0
Behavior3/5

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

With no annotations, the description carries the burden of explaining behavior. It conveys that this is a semantic, non-exact search over session history, which is useful, but it does not disclose return format, result ordering, or any access or side-effect implications. This is adequate but not richly transparent.

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 front-loaded with the core purpose and immediately gives practical usage examples, followed by a compact parameter list. The second sentence is somewhat redundant with the first, but overall it is efficient and scannable.

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?

For a simple three-parameter search tool with an output schema, the description covers what the tool does, when to use it, and what each parameter means. It does not over-explain return values, which is acceptable given the output schema is present. It is complete enough for an agent to call it correctly.

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's Args section is the primary documentation. It explains all three parameters with examples and defaults, covering query, n_results, and project. Minor details like n_results bounds or project filtering semantics are not specified, but the description compensates well.

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 names a specific verb ('search') and a specific resource ('Claude Code chat session history'), and clarifies that it finds past sessions by topic or content. This clearly differentiates it from sibling code-search and codegraph 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?

The examples ('how did I solve X?', 'when did I work on Y?') give clear situations where this tool is appropriate. It does not explicitly state when not to use it or name alternatives, but the intended use cases are obvious enough.

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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