graphrag-ts-pg

remember_text

Queue caller-supplied text as a document in a workspace's knowledge graph. Use this when the text is not already on the server's filesystem: it indexes exactly the text you pass and never reads chat history, files or URLs. The text becomes citable source material for later query_graph answers. Indexing is asynchronous — follow up with get_index_job, and the text is not searchable until the job publishes a new snapshot. Returns: job_id, workspace_id, status ("queued"), queue_position.

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": {
      "text": "<text>"
    },
    "name": "remember_text"
  }
}

Input Schema

Name	Required	Description	Default
`text`	Yes	The document body. Markdown is supported; one call is one document, so split very large inputs yourself.
`source`	No	Optional label stored with the document and shown in citations.
`workspace_id`	No	Workspace that owns the graph, 1–63 chars of [a-z0-9-]. Use the same id for every operation on one corpus. Defaults to "default".	default

Schema Changelog

Changes observed during successful MCP inspections.

Changed3 schema fields changedNov 2, 2026

addedInput schema / properties / source / description

Added value: +"Optional label stored with the document and shown in citations."

addedInput schema / properties / text / description

Added value: +"The document body. Markdown is supported; one call is one document, so split very large inputs yourself."

addedInput schema / properties / workspace_id / description

Added value: +"Workspace that owns the graph, 1–63 chars of [a-z0-9-]. Use the same id for every operation on one corpus. Defaults to \"default\"."

First observedNov 1, 2026

TDQS

A4.7/5.0

Behavior5/5

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

With no annotations, the description carries the full burden and does it well: it discloses that indexing is asynchronous, that content is not searchable until a new snapshot publishes, that only the passed text is indexed, and what the response fields are. This is exactly the operational context an agent needs before queuing work.

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?

Purpose is front-loaded in the first sentence, followed by scoping, async behavior, and return fields in a logical order. No sentence is redundant and nothing is padded.

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

Completeness5/5

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

Covers the full lifecycle of a low-complexity ingest call: what is indexed, when it is usable, which tool to poll, and what the call returns. With 100% schema coverage and no output schema, no gap remains that would cause an incorrect invocation.

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 coverage is 100%, so text, source and workspace_id are already documented in the input schema, establishing a baseline of 3. The description adds the semantic constraint that the indexed content is exactly what is passed, but no per-parameter detail beyond that.

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 and resource: queue caller-supplied text as a document in a workspace's knowledge graph. It immediately distinguishes this ingest path from the filesystem-based sibling by saying it indexes exactly the text passed and never reads files or URLs.

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

Usage Guidelines5/5

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

Gives an explicit selection condition (use when the text is not already on the server's filesystem) and names the required follow-up tool get_index_job plus the downstream consumer query_graph. The when-to-use and the next step are both spelled out.

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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