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ctxstore — memory that follows you across models

by ai.ctxstore
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Search Facts

search_facts
Read-onlyIdempotent

Search your persistent memory for stored facts.

For session bootstrap, prefer load_context() or wake_status() — they pull your identity, conventions, and latest self-note deterministically. Use search_facts when you need semantic discovery across the fact corpus.

Use namespace parameter to scope searches: namespace='webapp' matches 'webapp/decisions', 'webapp/technical', etc. (prefix matching).

Search tips: use the words the fact was stored with. If you stored 'Postgres database', search for 'database' or 'Postgres', not 'data storage solution'.

Input Schema

NameRequiredDescriptionDefault
layerNoContext persistence layer: 0=identity, 1=architecture, 2=state, 3=session.
queryYesWhat to search for
top_kNoNumber of results
categoryNoFilter by category
namespaceNoFilter results to a specific namespace.
recollectNoWhen true (default), appends a RECOLLECTIONS section: facts cross-referenced from the top hits via related_facts and [[wiki]] links, ranked by association strength times outcome-weighted usefulness (reward/salience). Set false for plain semantic results only.
boost_recentNoWhen true (default), appends the 5 most recent facts for recency signal. Set false to get pure semantic results without recent-fact injection.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed4 schema fields changed
    • changedInput schema / properties / layer / description
      Previous value: -"Context persistence layer: 0=identity, 1=architecture, 2=state, 3=session. Pass 0-3 as integer or string."New value: +"Context persistence layer: 0=identity, 1=architecture, 2=state, 3=session."
    • addedInput schema / properties / layer / maximum
      Added value: +3
    • addedInput schema / properties / layer / minimum
      Added value: +0
    • changedInput schema / properties / layer / type
      Previous value: -[
-  "integer",
-  "string"
-]New value: +"integer"
  2. First observed

TDQS

A4.4/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false and openWorldHint=false, so the safety profile is fully covered without the description. The description's behavioral additions are thin: it discloses namespace prefix-matching semantics, but the RECOLLECTIONS/recent-fact augmentation behavior is already spelled out in the schema's recollect and boost_recent descriptions.

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 routing decision before the mechanics and breaks the rest into short scannable blocks (namespace scoping, search tips). It is slightly longer than strictly necessary and omits any mention of recollect/boost_recent, so effort is spent unevenly across the parameter set.

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?

For a 7-parameter, 1-required retrieval tool with no output schema and full schema coverage, the description supplies the missing routing context (when not to use it), namespace semantics and search strategy. There is no output schema to explain, and the output-shaping flags are documented in the schema, so nothing essential is left unstated.

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 100%, so a 3 is the floor and the schema already documents all seven parameters including the layer enum and the recollect/boost_recent toggles. The description goes slightly beyond that by explaining that namespace uses prefix matching ('webapp' matches 'webapp/decisions'), which the schema's one-line 'Filter results to a specific namespace' does 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 and resource ('Search your persistent memory for stored facts') and explicitly separates itself from the deterministic bootstrap siblings load_context() and wake_status(). An agent can route between semantic discovery and deterministic retrieval without opening any 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 ('when you need semantic discovery across the fact corpus') and when-to-prefer-alternatives ('For session bootstrap, prefer load_context() or wake_status()'). It even adds query-construction guidance ('use the words the fact was stored with'), which is unusually actionable.

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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