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Hug0x0

mcp-reunion

reunion_possession_search_association_grants

Find association grants awarded by Commune of La Possession for 2022 or 2023. Returns beneficiary, amount, purpose, and payment conditions. Supports transparency analysis and public spending research.

Instructions

Search subventions (grants) awarded to associations by the Commune of La Possession in 2022 or 2023 (separate annual datasets). Returns issuing authority, beneficiary association name, convention date, grant object/purpose, amount (EUR), nature, payment conditions, grant share %. Sorted by convention date descending. Useful for transparency analysis, association funding research, public-spending audit.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
yearYesGrant year dataset: "2022" or "2023" (each year has its own dataset)
beneficiaryNoBeneficiary association name substring search
min_amountNoMinimum grant amount in EUR (inclusive)
limitNoMax grants to return (1-500, default 50)
Behavior3/5

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

No annotations are provided, so the description carries the full burden. It discloses sorting by convention date descending and that each year has a separate dataset, but does not explicitly state idempotency, side effects, or authorization needs. It is adequate but not highly transparent.

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?

The description is a single, well-structured paragraph that packs essential information without redundancy. Every sentence adds value.

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 4 parameters and no output schema, the description covers all parameters, return fields, sorting, and dataset years. It is complete enough for an agent to invoke the tool correctly.

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 good parameter descriptions. The description adds value by clarifying that 'year' selects a separate dataset, 'beneficiary' is a substring search, 'min_amount' is inclusive, and 'limit' defaults to 50 with a max of 500. This goes beyond 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 clearly states it searches subventions (grants) awarded to associations by the Commune of La Possession for specific years (2022/2023) and lists the fields returned. This distinguishes it from siblings like reunion_possession_search_procurement (procurement) and reunion_search_associations (general associations).

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 description mentions it is 'Useful for transparency analysis, association funding research, public-spending audit,' providing clear context. It does not explicitly state when not to use or list alternatives, but the specificity implies appropriate usage.

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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