RE-call MCP Memory Server

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recall_search

Read-onlyIdempotent

Search prior agent memory before acting to retrieve past decisions, hypotheses, and advice with trust verdicts, so you can avoid re-litigating closed work and know when to abstain.

Instructions

Search the agent's OWN memory before acting, and get actionable guidance.

    Call this before proposing an idea, forming a hypothesis, or repeating past work:
    if a closed decision or falsified hypothesis surfaces, do not re-litigate it. Every hit
    carries a trust verdict (only `ok` hits should be relied on), a calibrated confidence,
    provenance (indexed_at) and validity (superseded_by / valid_until). When `abstained` is
    true, NO valid hit survived — say you don't know instead of answering from the hits.
    `advice` states what to do.

    Args:
        query: what to recall (natural language).
        source: optional source filter (only search one file/source).
        k: max hits to return (default 5). Under a fast or quality process profile this is
            clamped DOWN to the profile's returned count and is never raised: the cost
            profile is chosen per process, not per request.

    Returns:
        JSON with abstention, calibration status and ID, tenant/generation/pipeline/corpus/
        query-set identities, freshness, advice, and hits carrying provenance and verdicts,
        plus per-stage timings, `total_ms`, `latency_budget_ms` (null when no budget is
        enforced) and `budget_exceeded`.

    Raises:
        RetrievalOverloaded: the process has no capacity to begin this retrieval within its
            latency budget. Retryable and free: nothing was embedded and no state changed.
            Carries `reason` (`queue_full` | `budget_exhausted`) and `retry_after_seconds`.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`k`	No
`query`	Yes
`source`	No

Output Schema

TableJSON Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.9.2Aug 10, 2026

TDQS

A5/5.0

Behavior5/5

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

The description goes far beyond the annotations (readOnly, idempotent) by disclosing the trust verdict system, abstention semantics, confidence calibration, provenance indexing, validity fields, and the clamping of k. It also details the RetrievalOverloaded error, including that it is retryable, free, and changes no state. This rich behavioral context is highly valuable for correct invocation and result interpretation.

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 well-structured with clear sections for Args, Returns, and Raises. The core action is front-loaded in the first sentence. While lengthy, every sentence contributes either to usage guidance, parameter semantics, or behavioral expectations, making it efficient for the complexity of the tool. It is not redundant with the schema or annotations.

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?

Given the tool's complexity, the description covers all necessary aspects: usage context, parameter details, return payload structure, error modes, and safety guarantees (e.g., 'nothing was embedded and no state changed'). It is self-contained and leaves no significant gaps for an agent to infer or guess.

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

Parameters5/5

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

With schema_description_coverage at 0%, the description is the sole source of parameter meaning. It thoroughly explains 'query' as natural language, 'source' as an optional filter, and 'k' as a max hits with the default and clamping behavior under cost profiles. This fully compensates for the missing schema descriptions and adds critical context about k's dynamic adjustment.

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: 'Search the agent's OWN memory before acting, and get actionable guidance.' The verb 'search' and resource 'agent's OWN memory' are specific, and it distinguishes from sibling tools by focusing on recall/search versus other memory operations. The context 'before proposing an idea, forming a hypothesis, or repeating past work' further clarifies its unique role.

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?

The description explicitly instructs when to call the tool ('Call this before proposing an idea, forming a hypothesis, or repeating past work') and provides post-call guidance on interpreting hits, including the abstention case ('say you don't know instead of answering from the hits'). It also explains the k parameter clamping under different process profiles, helping the agent set expectations. No explicit 'when not to use' is stated, but the strong 'call this before' guidance suffices.

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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