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PlainTxtOffice

Plain Text Memory MCP

Read memory graph

read_graph
Read-onlyIdempotent

Retrieve every entity and relation from the repository's shared knowledge graph, or filter by date range or agent to see only matching entries.

Instructions

Read every entity and relation, or only those in a date range.

Returns the whole graph, which grows with the repo's memory. With since, before, or agent, returns only entries whose tags match: an entity appears when it or any of its observations matches, with only the matching observations. Any filter leaves out untagged entries. To read part of the graph by topic, see search_nodes and open_nodes.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
agentNoOnly entries written by this agent, such as claude-code or codex.
sinceNoOnly entries added at or after this time: an ISO date such as 2026-10-01, or a timestamp; local time when no offset is given.
beforeNoOnly entries added before this time, in the same format as since.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
entitiesYes
relationsYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.2.0

TDQS

A4.6/5.0
Behavior5/5

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

Annotations already declare readOnly/idempotent/non-destructive, so the safety profile is covered. The description goes further and discloses non-obvious filter semantics: matching is tag-based, an entity is included when it or any observation matches (returning only matching observations), and any filter drops untagged entries. It also warns the graph 'grows with the repo's memory'.

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?

Front-loads the core action ('Read every entity and relation, or only those in a date range') before the filtering detail. It is information-dense but every sentence carries meaning; the tag-matching sentence is slightly convoluted but not wasteful.

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?

An output schema exists, so return values need no explanation. The description covers purpose, filter semantics, and alternatives, leaving nothing an agent needs in order to call it correctly.

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 the schema already documents agent, since, and before, making the baseline 3. The description adds some meaning beyond the schema by explaining that these filters operate on tags and that observations are filtered along with entities, which the per-parameter descriptions do not convey.

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 (Read) and resource (entity and relation graph) with clear scope: the whole graph or a filtered subset. It explicitly contrasts itself with search_nodes and open_nodes, so an agent can distinguish it from siblings without opening a schema.

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 explicit when-to-use: read everything, or narrow by date/agent filters. It also names the alternatives ('To read part of the graph by topic, see search_nodes and open_nodes'), routing the agent to the right tool for topic-scoped reads.

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