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CPersona

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by Cloto-dev

recall

Read-only

Retrieve relevant memories with multi-strategy search (vector + FTS5 + keyword). Returns content as previews by default; expand rows for full message text.

Instructions

Recall relevant memories using multi-strategy search (vector + FTS5 + keyword). Message content is returned as a preview tier by default — expand selected rows with get_contents(refs), or opt out wholesale with full_content=true. full_content is itself budgeted (200k chars per response, bug-211): rows past the budget degrade to the preview tier and the response carries full_content_budget_chars (absent when the budget never bites). v2.5.2 additive: each scored message carries match_reason={signal, score, ...} where signal is the branch the ranking / quality gate keyed on (confidence > rsf > cosine > rrf) and the remaining keys (cosine / rrf / rsf) surface the internal per-retriever contributions present on that row. Unscored rows (cascade FTS/keyword) omit match_reason. A response carrying gate_fallback=true (absent otherwise) means every candidate fell below the quality gate and the below-gate lexical matches were returned instead of an empty result — treat them as low-confidence.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
deepNoDeep recall — disable time and completion decay for exhaustive search
limitNoPer-retriever search depth, not a pure response cap (CSC #716): the value is handed to each retrieval channel (vector / episode FTS / keyword) as its top-K, so lowering it shrinks the candidate pool itself — rows beyond the depth are unreachable at any gate value, and score normalization / autocut operate on the smaller pool, which can also reorder what remains. Fewer rows than this may be returned. (Agent-facing cap; the library layer accepts up to the scan window for direct callers.)
queryYesSearch query (empty returns recent memories)
channelNoFilter memories by channel (e.g. 'chat', 'discord'). Default: '' (all channels).
agent_idYesAgent identifier
source_idNov2.4.20 per-user source filter. Empty (default) = no filter. Non-empty = prefix match against json_extract(source, '$.id'), e.g. 'discord:12345' to restrict to one Discord user, or 'discord:' to scope to all Discord-sourced memories. Episodes carry no per-user source tagging, so they are skipped when this is set — UNLESS channel is also set, which scopes episodes to one conversation and re-admits them.
project_idNov2.4.17 γ filter. Omit → no filter (all projects). '' → global pool only. 'X' → 'X' bucket ∪ global pool. Threaded through cascade / RRF / vector / FTS / keyword paths. v2.5.1: pass '@auto' to resolve this agent's default from the server's operating context (the resolution is echoed as resolved_project_id; an unmapped agent yields operating_context_warning). bug-186: resolution requires a configured operating context. With none — the default, and equally the outcome of a sidecar that fails to parse — the sentinel is NOT resolved: it is stored and filtered as the literal project_id '@auto', resolved_project_id echoes '@auto', and no warning is raised. Read resolved_project_id before relying on the resolution.
full_contentNov2.5.0 preview tier opt-out. By default message content longer than the preview cap (CPERSONA_RECALL_PREVIEW_CHARS, default 500) is returned as a pure prefix with content_truncated/content_len markers; each message's `ref` expands via get_contents. true returns full text.
exclude_contentsNoNormalized content strings to exclude from results (starts-with match). Used to prevent duplication with conversation context already known to the caller.
Behavior5/5

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

The description goes well beyond the readOnlyHint annotation by disclosing default preview-tier content, the 200k-character full_content budget behavior, match_reason signal branches, and gate_fallback semantics. This gives the agent an accurate model of the tool's runtime behavior and edge cases.

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?

The description is dense and front-loaded with the core purpose, and every sentence adds information. It is not short, but the length is justified by the tool's complexity; minor version/issue references add noise but do not undermine readability.

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?

Without an output schema, the description explains critical response behaviors: match_reason content and signal ordering, unscored rows, gate_fallback, and full_content_budget_chars. It also ties the result preview/ref flow to get_contents, making the tool usable for an agent.

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 coverage is 100% with detailed parameter descriptions, so the baseline is 3. The description adds meaningful extra semantics for full_content by explaining the response-level budget and degradation to preview tier, which is absent from the schema.

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 opens with 'Recall relevant memories using multi-strategy search (vector + FTS5 + keyword)', giving a specific verb, resource, and method. This clearly distinguishes the tool from sibling tools like list_memories or store, and the preview-tier/get_contents relationship adds further operational clarity.

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

Usage Guidelines3/5

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

The retrieval-focused first sentence implies when to use the tool, and get_contents is mentioned for expanding selected rows. However, there is no explicit guidance about when to choose recall over recall_with_context, list_memories, or other memory tools, nor any exclusion criteria.

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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