list_open_deliberationsList this user's open deliberations so an authenticated AI can discover work without being handed a UUID or canonical key. Use target_model to find work addressed to a model label and unclaimed_only before claiming a task. The gpt and chatgpt labels are treated as aliases.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| target_model | No | Model inbox label; gpt and chatgpt are aliases. | |
| unclaimed_only | No |
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?
Annotations already establish read-only, idempotent, and non-destructive behavior. The description adds meaningful extra context: results are scoped to the current user, model label aliases are normalized, and the tool exists to avoid requiring an identifier. No contradiction with annotations.
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?
Three sentences, each earning its place: it states the resource and purpose, gives filtering guidance tied to a sibling workflow, and records alias behavior. There is no padding or repetition.
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?
For a read-only listing tool with no required parameters, the description is sufficient to guide correct invocation. It does not describe the return shape, but no output schema is provided and the list semantics are clear enough to make that omission acceptable.
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 low at 33%, but the description compensates for target_model and unclaimed_only by giving them operational meaning. The limit parameter still relies only on its schema defaults and bounds, so its semantics are not significantly deepened.
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?
States a specific verb and resource: 'List this user's open deliberations.' The description also explains the purpose is discovery without a UUID or canonical key, which clearly distinguishes this from sibling tools like get_deliberation and resolution-oriented 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?
Provides clear contextual guidance: use target_model to filter by model label and unclaimed_only before claiming work. It also records the alias behavior for gpt and chatgpt. However, it does not explicitly say when an alternative like get_deliberation should be preferred when a UUID is already available.
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.
Tools cluster into clear functional families — classification, location, reviews, deliberations, and system — and potentially overlapping operations are explicitly cross-referenced (e.g., enrich_subject vs correct_subject_fact, affirm vs propose reclassification). The main hazard is the resolve_subject / resolve_subject_type / resolve_subject_hierarchy trio, whose near-identical prefixes could mislead an agent at first glance despite well-written descriptions.
The surface is dominated by a consistent snake_case verb_noun pattern with stable verb families: get_*, list_*, resolve_*, set_*, save_*, create_*, register_*, and submit_*. Minor deviations — bare-verb fetch and search, and the noun-led vocabulary_index — break the pattern slightly but do not obscure it.
34 tools is heavy and above the preferred range, and the classification family alone accounts for ten tools with substantially duplicated vocabulary guidance. The count is partially earned, however, because the server genuinely spans several subsystems — reviews, subject classification, location assertions, deliberations, and governance — each with its own lifecycle.
Each subsystem has thorough lifecycle coverage: deliberations (create/claim/contribute/get/list/resolution), reviews (save/fetch/delete/list/visibility), location (assert/list/resolve), and classification (propose/affirm/reopen/relationships/aliases). Minor gaps include no way to edit review content, no direct list-all-subjects endpoint, and no retirement path for fields or aliases.