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hippocampus-mcp

by hrmtz
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Python
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search_ghost_memory

Search accumulated agent rules and feedback across projects. Rank results by activation, relevance, and recency. Empty query lists top-ranked memories.

Instructions

Search cross-project agent ghost memories (= this agent's rule/feedback accumulation).

Empty query → list top-ranked memories (= "what's in my ghost vault" overview). Non-empty query → hybrid FTS + vector semantic ranking via agent.search_ghost_ranked (SECURITY DEFINER, migration 020).

⚠️ current_project is caller-attested, NOT server-verified. shared-restricted requires current_project in per-memory allowlist (= pentest/commercial boundary).

Ranking: rank_score = base_score * recency_factor + semantic_sim * 0.5 base_score = 0.2activation + 0.5incident_prevention + 0.3endorsement - 0.4correction - 0.2pred_error + 0.1scope_bonus recency_factor = exp(-days_since_last_activated / 30) semantic_sim = 1 - cosine_distance(query_vec, memory.dense) (0 if empty query)

Each returned row triggers agent.bump_activation, so memories that surface in search naturally rise in rank over time (= self-tuning loop).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryNo
current_projectNo
n_resultsNo
include_restrictedNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Tool Definition Quality

A4.7/5.0
Behavior5/5

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

Extremely detailed: explains ranking formula, activation bump, self-tuning loop, and security limitations. No annotations present, so description carries full burden.

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?

Well-structured with sections but somewhat verbose. Every sentence adds value, though could be slightly more concise.

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?

Thorough coverage of ranking, security, self-tuning; output schema exists for return values. Complete for a tool of this complexity.

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?

Schema has 0% description coverage, but description explains all 4 parameters: query behavior, current_project attestation, n_results, include_restricted boundary.

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?

Specific verb 'search' and resource 'ghost memories' clearly defined. Distinguishes from sibling tools which all deal with conversations.

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

Usage Guidelines4/5

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

Provides clear guidance on empty vs non-empty query behavior and security caveats. No explicit alternatives but siblings are unrelated.

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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