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Pseudogiant-xr

PseudoLife-MCP

Official

document_ingest

Destructive

Index a .txt, .md, or .pdf file into a reference bank for background documents, keeping them separate from conversational memory so they remain retrievable by cosine similarity.

Instructions

Index a file into the reference bank — a separate store for background documents (papers, manuals, codebases) retrieved by pure cosine similarity, kept apart from conversational memory.

Returns: {source, chunks_stored, chunks_total}.

Input Schema

NameRequiredDescriptionDefault
pathYesPath to a .txt / .md / .pdf file, resolved on the SERVER's filesystem — with the Docker daemon, a path visible inside the container (e.g. a mounted volume), not a host path.
sourceNoSource tag for the chunks; defaults to the filename.

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changedv0.18.1
    • addedInput schema / additionalProperties
      Added value: +false
  2. Changed2 schema fields changedv0.15.0
    • addedInput schema / properties / path / description
      Added value: +"Path to a .txt / .md / .pdf file, resolved on the SERVER's filesystem — with the Docker daemon, a path visible inside the container (e.g. a mounted volume), not a host path."
    • addedInput schema / properties / source / description
      Added value: +"Source tag for the chunks; defaults to the filename."
  3. First observedv0.11.0

TDQS

A3.9/5.0
Behavior3/5

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

Annotations already declare destructiveHint=true, idempotentHint=false and readOnlyHint=false, so the safety profile is covered. The description adds the conceptual context of a separate cosine-similarity store, but it does not explain the notable implications of the annotations: what is 'destructive' about ingesting, and whether re-ingesting the same file duplicates chunks given idempotentHint=false. Useful but incomplete beyond the structured hints.

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?

Two sentences, front-loaded with purpose and a compact clarifying phrase, then a short returns line. Efficient overall, though the Returns enumeration is largely redundant given that an output schema exists.

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?

Purpose, storage context and return shape are covered, and an output schema exists so return values need not be spelled out. The one real gap is behavioral: for a non-idempotent, destructive-flagged ingestion tool the description never says whether repeated ingest of the same file duplicates or replaces chunks.

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

Parameters3/5

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

Schema description coverage is 100%, so both parameters (path with its server-side filesystem caveat, and the source tag defaulting to filename) are already fully documented in the schema. The description adds no parameter-level detail, which is the expected baseline when the schema does the work.

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?

States a specific verb (Index) and resource (a file into the reference bank) and immediately characterizes the destination as a distinct store for background documents, differentiating it from conversational memory and from the sibling document_search (which retrieves rather than ingests). An agent can tell what this does without opening the schema.

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 contrast with 'conversational memory' clearly implies when to route here (background corpora like papers, manuals, codebases) versus the many memory_* siblings, and names example document types. It stops short of an explicit when-not or a named alternative tool, so it is strong context rather than full routing guidance.

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