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total-agent-memory

total-agent-memory

by vbcherepanov
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
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ingest_codebase

Parse files or directories into semantic AST chunks (functions, classes, methods) across 8 languages, returning chunk counts and samples for indexing into persistent agent memory.

Instructions

Parse a file or directory into semantic AST chunks (functions, classes, methods) across 8 languages. Returns chunk count + sample.

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_codebase"
  }
}

Input Schema

NameRequiredDescriptionDefault
pathYes
includeNoExtension allowlist e.g. ['.py','.go']
sample_limitNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

B3.2/5.0
Behavior2/5

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

Annotations indicate readOnlyHint=false and destructiveHint=false, but the description does not disclose any side effects (e.g., whether it writes to a database or modifies files). It only mentions parsing and returning data, leaving behavioral traits ambiguous and not adding clarity beyond the minimal annotations.

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, using two clear sentences without redundant information. It efficiently conveys the core action and output, maintaining a clean structure that is easy to parse.

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?

Given the tool's simplicity and lack of an output schema, the description provides a basic understanding of the return (chunk count + sample) but omits details like the exact output format or error conditions. This is a gap for an agent that needs to interpret results reliably, so completeness is only moderate.

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?

The schema has three parameters (path, include, sample_limit), but only 'include' has a description. The description does not elaborate on any parameter meanings, and with schema coverage at only 33% (low), the description fails to compensate, leaving the required 'path' and 'sample_limit' under-specified.

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's function: parsing files or directories into semantic AST chunks across 8 languages, and it explicitly mentions what it returns (chunk count + sample). This distinguishes it from sibling tools, which are memory-related, making the purpose unmistakable.

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 code parsing but does not explicitly state when to use this tool versus alternatives. Since all siblings are memory/workflow tools, the context makes usage obvious, but no explicit guidance on when not to use it is provided.

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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