Skip to main content
Glama
Cloto-dev

CPersona

Official
by Cloto-dev

recall

Read-only

Retrieve relevant memories with multi-strategy search combining vector, keyword, and full-text. Returns message previews by default, with an option to request full content for deeper context.

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 — halves the quality gate (and the calibrated fused gate), so weaker matches are admitted. It also disables time and completion decay, which are inert unless CPERSONA_CONFIDENCE_ENABLED=true, and it does NOT widen the scan window (CPERSONA_MAX_MEMORIES) — deep is about how weak a match may be, not how far back the search reaches.
limitNoPer-retriever search depth, not a pure response cap: 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.
session_keyNoOpaque session identity you declare — a partition hint, NOT authentication. It scopes this process's per-session state: the degraded-recall advisory's "already told you" memory, and which no-persist pause applies to this call. It does NOT filter stored data (use agent_id / project_id / channel for that), and it never reaches the database. Omit it to share one bucket with every other caller that omits it, which is the behaviour that predates this parameter.
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.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Changed2 schema fields changedv2.5.10
    • changedInput schema / properties / limit / description
      Previous value: -"Per-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.)"New value: +"Per-retriever search depth, not a pure response cap: 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.)"
    • addedInput schema / properties / session_key
      Added value: +{
      +  "default": "",
      +  "description": "Opaque session identity you declare — a partition hint, NOT authentication. It scopes this process's per-session state: the degraded-recall advisory's \"already told you\" memory, and which no-persist pause applies to this call. It does NOT filter stored data (use agent_id / project_id / channel for that), and it never reaches the database. Omit it to share one bucket with every other caller that omits it, which is the behaviour that predates this parameter.",
      +  "type": "string"
      +}
  2. Changed1 schema field changedv2.5.6
    • changedInput schema / properties / deep / description
      Previous value: -"Deep recall — disable time and completion decay for exhaustive search"New value: +"Deep recall — halves the quality gate (and the calibrated fused gate), so weaker matches are admitted. It also disables time and completion decay, which are inert unless CPERSONA_CONFIDENCE_ENABLED=true, and it does NOT widen the scan window (CPERSONA_MAX_MEMORIES) — deep is about how weak a match may be, not how far back the search reaches."
  3. Changed1 schema field changedv2.5.4
    • changedInput schema / properties / limit / description
      Previous value: -"Max memories to return (agent-facing cap; the library layer accepts up to the scan window for direct callers)"New value: +"Per-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.)"
  4. Addedv2.5.2
  5. Removedv2.5.1
  6. Changed2 schema fields changedv2.4.34
    • addedInput schema / properties / project_id
      Added value: +{
      +  "description": "v2.4.17 γ filter. Omit → no filter (all projects). '' → global pool only. 'X' → 'X' bucket ∪ global pool. Threaded through cascade / RRF / vector / FTS / keyword paths.",
      +  "type": "string"
      +}
    • addedInput schema / properties / source_id
      Added value: +{
      +  "default": "",
      +  "description": "v2.4.20 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 are skipped when set (no per-user source tagging).",
      +  "type": "string"
      +}
  7. Changed1 schema field changedv2.4.10
    • addedInput schema / properties / exclude_contents
      Added value: +{
      +  "description": "Normalized content strings to exclude from results (starts-with match). Used to prevent duplication with conversation context already known to the caller.",
      +  "items": {
      +    "type": "string"
      +  },
      +  "type": "array"
      +}
  8. First observedv0.1.0

TDQS

A4.1/5.0
Behavior5/5

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

Beyond the readOnlyHint annotation, the description discloses substantial behavioral details: content is returned as a preview tier by default, full_content is budgeted at 200k chars, rows past the budget degrade, match_reason and gate_fallback appear conditionally, and unscored rows omit match_reason. It also exposes edge cases with bug IDs and version markers, giving an agent a genuinely accurate model of the tool's behavior.

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 long and dense, but every sentence carries operational weight: purpose, preview behavior, budget semantics, match_reason encoding, and gate_fallback handling. It is front-loaded with the core purpose and then structured into logical behavioral blocks, though some version-specific details add reading load.

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?

With no output schema and minimal annotations, the description carries the full burden of explaining return behavior. It covers result tiers, budget degradation, per-row match_reason structure, and the gate_fallback failure mode. For a tool this complex, the description is unusually complete for invocation and interpretation.

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%, so the baseline is 3. The description adds meaningful extra context for full_content, including the 200k-char budget and the full_content_budget_chars response field, which is not present in the schema description. This additional semantic detail justifies a score above baseline.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

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 clear verb, resource, and method. It does not explicitly distinguish itself from the sibling tool recall_with_context, but its scope and mechanism are clearly stated.

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 description gives useful guidance on when to use get_contents vs full_content for expanding message content, and explains the default preview-tier behavior. However, it never explicitly addresses when to choose this tool over recall_with_context or other sibling recall-related tools, leaving that comparison to inference.

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

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/Cloto-dev/CPersona'

If you have feedback or need assistance with the MCP directory API, please join our Discord server