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Python
Hybrid

store_finding

Record durable findings like discoveries, root causes, or corrections to shared memory for later retrieval. Use for knowledge worth persisting; for task outcomes, use record_result instead.

Instructions

Primary workflow name for recording a finding to shared memory; implemented by knowledge(action='store'). Use for durable knowledge — a discovery, root cause, or correction. For task/tool/test outcomes use record_result instead.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
tagsNoTags (action=store, note); for action=search an exact any-of filter in every search mode.
contentNoExtended content/details (for action=store or action=note)
detailsNoExtended details for discovery (for action=store). Alias: content
summaryNoDiscovery summary (for action=store)
agent_idNoFilter by agent (for action=get, search; omit when using discovery_id readback)
severityNoSeverity: low, medium, high, critical (for action=store or action=update)
task_labelNoS22 H5 provenance: human-readable bounded task label
task_outcomeNoS22 H5 provenance: outcome label for the bounded task
comparison_keyNoS22 H5 provenance: stable key for comparing the same bounded task across harnesses
discovery_typeNoaction=store; one of architectural_decision, learning, pattern, bug_fix, refactoring, documentation, experiment, question, note, rule, insight, bug_found, bug, improvement, exploration, observation.
memory_contextNoS22 provenance: memory/KG/transcript surfaces visible to the writer
continuity_tokenNoOwnership proof from onboard()/identity(), for same-live-process rebinds only. Not a cross-process resume credential.
client_session_idNoIn-session binding id from start_session()/identity(); pass it on same-process calls. Not a cross-process proof.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.9/5.0
Behavior2/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool records to shared memory, but does not disclose what happens after the call (e.g., return value, readback behavior), whether the operation is destructive or idempotent, whether permissions are required, or what failure modes exist. For a persistent write operation, this is a significant gap.

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?

Three compact sentences, with the core purpose first and the routing alternative immediately after. No filler or redundant restatement of the name. The implementation detail 'implemented by knowledge(action=\'store\')' earns its place by linking the tool to the underlying mechanism.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description is adequate for a high-level understanding, but the tool has 13 parameters, no annotations, and no output schema. It does not explain what the caller should expect after invoking the tool, whether any parameters are effectively required for a meaningful store, or how this interacts with the sibling knowledge and update_finding tools beyond record_result. Given the complexity, the description is thinner than ideal.

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

Parameters3/5

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

Schema description coverage is 100%, so the baseline is 3. The description reinforces the conceptual purpose of the parameters (durable knowledge, discovery, root cause, correction) but does not add any parameter-specific meaning beyond what the schema already provides.

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 names a specific verb and resource ('recording a finding to shared memory') and explicitly distinguishes the tool from record_result by stating durable knowledge vs task/tool/test outcomes. It also clarifies the underlying implementation via knowledge(action='store'), leaving no ambiguity about what the tool does.

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 gives explicit when-to-use guidance: 'Use for durable knowledge — a discovery, root cause, or correction.' It also names the alternative for other cases: 'For task/tool/test outcomes use record_result instead.' This is a clear and actionable routing rule.

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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