列园区新闻
list_park_news园区新闻 feed(开园 / 招商 / 补贴变化等时效信息)。可按城市或具体园区过滤。
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| city | No | 城市过滤 | |
| limit | No | 返回条数,默认 30 | |
| parkId | No | 某园区 id 过滤 |
list_park_news园区新闻 feed(开园 / 招商 / 补贴变化等时效信息)。可按城市或具体园区过滤。
| Name | Required | Description | Default |
|---|---|---|---|
| city | No | 城市过滤 | |
| limit | No | 返回条数,默认 30 | |
| parkId | No | 某园区 id 过滤 |
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=true, idempotentHint=true, and destructiveHint=false, so the safety profile is covered. The description adds content context (news categories) but no additional behavioral traits such as pagination behavior, sorting, or output structure, so it adds limited value beyond 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?
The description is a single concise sentence that front-loads the resource type ('园区新闻 feed') and includes relevant examples of content categories. Every clause earns its place; there is no redundant information.
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 simple read-only list tool with fully documented optional parameters and rich annotations, the description provides sufficient context for an agent to know what the tool does and how to filter. The lack of an output schema is somewhat mitigated by the 'feed' framing, though explicit details about result ordering or fields would make it more complete.
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%, with each parameter (city, limit, parkId) already documented in the input schema. The description's mention of filtering by city or park simply restates the schema notes without adding new semantics, so the baseline of 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 clearly identifies the tool as a park news feed ('园区新闻 feed') with specific content examples (开园 / 招商 / 补贴变化等时效信息), making the resource and operation clear. It is implicitly distinct from sibling list tools dealing with parks, stats, or policies, though it does not explicitly name them.
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 description provides clear context by explaining that this is for time-sensitive park news and that it can be filtered by city or specific park ('可按城市或具体园区过滤'). It does not explicitly state when to avoid this tool or mention alternatives, so it falls short of a 5.
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.