园区城市概览
list_park_city_stats各城市园区总数 + 已运营数(按总数倒序),宏观选址用。传 benefitType 则只统计含该补贴的园区,与列表口径一致。
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| benefitType | No | 只统计含某类补贴的园区 |
list_park_city_stats各城市园区总数 + 已运营数(按总数倒序),宏观选址用。传 benefitType 则只统计含该补贴的园区,与列表口径一致。
| Name | Required | Description | Default |
|---|---|---|---|
| benefitType | No | 只统计含某类补贴的园区 |
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 declare readOnlyHint, openWorldHint, idempotentHint, and non-destructive behavior. The description adds valuable behavioral detail beyond annotations: aggregation dimensions, descending sort order by total count, and the claim that filtering is consistent with the list endpoint's criteria. It stops short of specifying the response envelope, but for a simple aggregate read this is adequate.
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 dense clauses deliver the core metric, sort order, intended use, and optional filter behavior with no filler. The purpose is front-loaded and every phrase 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?
For a one-optional-parameter, read-only aggregate tool, the description fully covers what is counted, how it is sorted, when it is appropriate, and how the optional parameter changes results. No output schema exists, but the description states the output dimensions sufficiently for an agent to invoke and interpret the call.
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 coverage is 100% and the enum already documents benefitType's meaning. The description reinforces it and adds cross-tool consistency ('与列表口径一致'), which clarifies that the same filter semantics apply as in the corresponding list operation. This is a meaningful addition beyond the schema.
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 ('list/count') and resource ('park city stats'), and defines the exact outputs: total parks per city plus operating count, sorted by total descending. The phrase '宏观选址用' (macro site selection) separates it from sibling park listing endpoints like list_parks and list_park_news.
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?
Gives clear context: use for macro site selection, and describes the optional benefitType filter behavior. It does not explicitly name alternative tools or state when not to use it, but the intended use case is unambiguous.
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.
Each tool has a clearly documented purpose, often with explicit 'when to use' guidance and cross-references, making the vast majority easy to tell apart. A few clusters (get_my_brief, get_my_positioning, get_my_work, get_my_dispatch) and data-overlapping get_my_card vs get_my_profile require careful reading, but descriptions are detailed enough to prevent serious misselection.
The overwhelming majority follow snake_case verb_noun conventions (create_product, update_need, list_my_signups). Minor deviations include noun-only feed names (personalized_feed, random_feed), inconsistency between 'prefs' and 'preferences' in notification tools, and a mix of update_* and set_* for mutations, but the pattern remains predictable overall.
137 tools is an extreme mismatch for any MCP server, far exceeding the 50+ threshold for a score of 1. Even with a broad multi-domain platform, this volume makes tool selection and navigation impractical and heavily burdens the agent's context window.
The surface covers full lifecycles for needs, products, activities/signups, conversations, collaboration goals/tasks, dispatch, profile/onboarding, and supporting resources like companies, parks, policies, and ratings. Deliberate omissions (no user-post creation, no organizer profile editing via agent) are explicitly documented, so core workflows have no obvious dead ends.