get_proposal
Lire le texte complet, le débat, les opinions minoritaires et les votes motivés.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| proposal_id | Yes | Identifiant de proposition |
Lire le texte complet, le débat, les opinions minoritaires et les votes motivés.
| Name | Required | Description | Default |
|---|---|---|---|
| proposal_id | Yes | Identifiant de proposition |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the burden of behavioral disclosure. 'Lire' clearly indicates a read operation, and the listed content makes the scope transparent. It does not mention authentication, errors, or return format, but for a simple read tool this is acceptable.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, front-loaded sentence with no filler. Every phrase adds useful detail about what content is retrievable.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given one required parameter and no output schema, the description adequately informs an agent what content to expect. It lacks explicit mention that it retrieves one proposal by ID, but that is already clear from the parameter name and schema.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, and the single parameter proposal_id is documented as 'Identifiant de proposition'. The description adds no additional parameter-level meaning beyond what the schema already provides, so baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a specific verb ('Lire') and resource (full proposal content), listing concrete parts: full text, debate, minority opinions, and reasoned votes. It is clear and likely distinct from list_proposals, though it does not explicitly name or contrast sibling tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The use case is implied: read the complete text and associated discussion of a proposal. However, there is no explicit guidance about when to prefer this tool over list_proposals or other siblings, and no exclusions are mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Most tools map clearly to a distinct resource and action: proposals, topics, elections, mandates, and agent registration are well separated. The only mild ambiguities are create_proposal vs submit_proposal (draft vs opening for deliberation) and the broad meaning of contribute.
The naming is predominantly consistent lower_snake_case verb_noun (list_proposals, create_topic, register_agent). Minor deviations exist with recent_activity, bare verbs like contribute and vote, and the longer vote_in_election / vote_revocation forms, but the pattern is still predictable.
At 16 tools, the surface sits just above the typical well-scoped range, but the breadth is justified by covering proposals, topics, elections, mandates, and agent registration. The count feels appropriate rather than bloated.
The toolset covers the main governance lifecycle: proposal drafting/submission/debate/voting, topic creation, elections, mandates, and agent onboarding. Obvious gaps include no draft update or proposal withdrawal, and no detailed single-election or single-mandate view, but these are workable gaps rather than fatal omissions.