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Get saved-Gambit activity

get_mandate_activity
Read-onlyIdempotent

What Gambit found for YOUR saved Gambits, newest first (limit 1-20, default 10) — the call for "what did my saved Gambits find today". Each row: mandate_id and mandate_version (which saved Gambit, and which edit of it), symbol / security_id / subject_id (what moved), observed_at, relevance_reason (why Gambit judged it relevant to that saved Gambit), activity_reasons (codes for what triggered it: drawdown, positive_extension, price_and_volume, or a secondary-source signal), evidence (the market data behind it), status (where Gambit's review of it stands: pending or processing while it runs, ignored when judged not relevant, failed with last_error — a short code such as recommendation_failed), recommendation (the advisory Gambit drafted from it, when one was), case_id when it opened or updated one of your Gambits (pass it to get_gambit), and created_at / updated_at. (Requires api:read.)

Input Schema

NameRequiredDescriptionDefault
limitNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A4.7/5.0
Behavior5/5

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

Annotations already cover read-only/idempotent safety, and the description adds substantial context beyond them: newest-first ordering, the api:read auth requirement, status lifecycle (pending/processing/ignored/failed with last_error), and what evidence and recommendation fields represent. This is well past what structured fields provide.

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?

Purpose and parameters are front-loaded, and every clause about row fields earns its place given there is no output schema. It is one very dense run-on sentence, though, which slightly hurts scanability.

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, the description must document the return shape — and it enumerates nearly every row field (mandate_id/version, symbol/security_id/subject_id, observed_at, relevance_reason, activity_reasons, evidence, status, recommendation, case_id, timestamps). Nothing an agent needs to call or interpret it is missing.

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 coverage is 0% for the single limit param, and the description fully compensates with 'limit 1-20, default 10', which matches the schema bounds and adds the default the schema omits.

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?

States a specific verb+resource ('What Gambit found for YOUR saved Gambits') with scope ('saved Gambits', newest first) that separates it from get_gambit, list_gambits, and get_gambit_history. The parenthetical 'what did my saved Gambits find today' pins the intent unambiguously.

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?

The framing 'the call for "what did my saved Gambits find today"' gives clear usage context, and it routes to get_gambit when a case_id is present. It doesn't explicitly exclude or contrast with siblings like get_gambit_history or list_gambits, so a small inference remains.

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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