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Park Tech Oriented Company Count

park_tech_oriented_company_num

基于具体园区名称科技型中小型企业数量查询。 涉及指标/类型:科技型中小型企业数量 不包含:其他企业分类的统计;企业名单明细 典型问法:中关村软件园科技型中小型企业有多少;张江高科技园区科技型中小型企业数量;苏州工业园区科技型中小型企业有多少家

Pricing: {"unit": "credits", "billing_model": "per_run", "per_run": 100, "unit_description": "optional"}

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
yearNo统计年份,如 2024;可选。
park_nameYes园区名称,如「中关村软件园」「张江高科技园区」「苏州工业园区」。

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
textYesText or Markdown response with the company count result. Also used for in-progress, failed, cancelled, or waiting-user messages.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

A4.1/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With only openWorldHint in annotations, the description adds meaningful behavioral scope by stating it returns counts only and excludes company list details and other classification statistics. The pricing block also transparently discloses the per-run credit cost of 100, which is useful operational context beyond the schema.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is front-loaded with the core purpose, followed by scoped exclusions, example questions, and pricing. Each section earns its place, though the pricing JSON adds some tail length. It is reasonably concise and structured for quick agent scanning.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple two-parameter tool with an output schema and openWorldHint annotation, the description is sufficiently complete. It covers the park-name basis, optional year via schema, scope exclusions, and cost. It could be more explicit about the park-vs-chain distinction, but the park-specific naming and examples make the context clear.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the baseline is 3. The description's typical questions provide example park_name values, but these largely mirror the schema examples already given. It does not add new semantic detail for the year parameter beyond what the schema already states.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description uses a specific verb+resource pattern: "基于具体园区名称科技型中小型企业数量查询" (query count of tech-oriented SMEs by park name). It clearly identifies the metric, excludes non-related statistics and company-level details, and provides typical question phrasings, which distinguishes it from sibling list/count tools.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description gives clear context for when to use this tool via the indicator type "科技型中小型企业数量" and explicitly states what it does NOT include: "不包含:其他企业分类的统计;企业名单明细." However, it does not explicitly name alternative tools like the list-returning sibling, so it stops short of full alternative guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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TDQS

B3.2/5.0
Disambiguation4/5

Most tools have clearly distinct purposes, with consistent scopes (e.g., chain_* vs park_* vs company_* vs gov_data_*). The list/num pairs are clearly differentiated. A few overlapping concepts exist (e.g., company_patent vs enterprise_change_innovation) but descriptions clarify the angle. Some typos (company_randomin_spection) don't cause ambiguity.

Naming Consistency4/5

Naming follows a mostly predictable snake_case pattern with prefixes indicating domain (chain_, park_, company_, enterprise_change_, gov_data_, poi_data_, business_surrounding_, cbd_surrounding_). Most tools use <prefix>_<entity>_<action> or <prefix>_<subject>. A few outliers (sg_chokepoint, tariff_calc, corporate_exception_report) deviate but are few and recognizable.

Tool Count1/5

With 198 tools, this is far beyond any reasonable scope for a single server. It exceeds even the 'extreme mismatch' threshold of 50+ tools. The large number makes selection and discoverability challenging, despite good internal organization.

Completeness4/5

The tool surface covers a vast range of enterprise data, regional macro stats, POI details, supply chain analysis, and tariffs. It appears to cover the primary domain comprehensively, with only minor potential gaps (e.g., no direct tool for company debt ratings or specific product catalogs, but these are addressed via enterprise_change_* and company_* tools).

Resources