list_my_mcp_interactionsList the authenticated user's structured, redacted MCP interaction telemetry. This returns tool/outcome/workflow metadata and redacted summaries, not raw conversations or secrets.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, and destructiveHint=false. The description adds meaningful behavioral context: results are scoped to the authenticated user, structured, redacted, and limited to metadata/summaries rather than raw content. This goes beyond what annotations convey.
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?
Two compact sentences with no filler. The first sentence states the action and object clearly; the second sentence clarifies content boundaries. Every clause earns its place.
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?
No output schema exists, but the description adequately tells the agent what will be returned (tool/outcome/workflow metadata and redacted summaries). It does not explain pagination, ordering, or limit behavior, but for a simple one-parameter list tool this is not a critical gap.
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 0% and the description does not mention the 'limit' parameter. While the parameter name and schema constraints (default 50, min 1, max 200) make its purpose inferable, the description provides no explicit semantics about how limit controls the returned telemetry.
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 uses a specific verb ('List') and a precise resource ('authenticated user's structured, redacted MCP interaction telemetry'). It also states exclusions ('not raw conversations or secrets'), which distinguishes it from sibling tools like list_my_workflows or list_open_deliberations.
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?
No explicit when-to-use guidance or alternatives are provided. The only exclusion ('not raw conversations or secrets') describes content boundaries, not decision criteria for when this tool should be selected over siblings. The agent is left to infer usage context from the tool name.
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 are separated by resource and action, but the classification cluster (affirm_subject_classification, propose_subject_reclassification, reopen_subject_classification, get_subject_classification) and the three resolve_subject* tools require close reading to distinguish. Long descriptions help, but an agent could easily pick the wrong member of those clusters.
The dominant pattern is verb_noun in snake_case (create_deliberation, list_reviews_by_visibility, resolve_subject_type, set_review_visibility). Minor deviations such as bare 'fetch' and 'search' and mixed verbs like affirm vs propose vs reopen are readable and do not break the convention.
34 tools substantially exceeds the 25+ threshold even for a server with multiple subdomains. The many classification, type, and location variants add cognitive load, and several could be consolidated or hidden behind a smaller workflow-oriented surface.
The set covers the main lifecycles: review save/fetch/assess/delete/visibility, subject resolution/enrichment/correction, classification with voting/reopening, deliberations, locations, and vocabulary management. Minor gaps remain, such as no direct full-subject getter and no update to review text, but these appear intentional in an immutable-review design.