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Glama

list_sectors

List all tracked sectors with startup counts.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

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

  1. First observed

TDQS

A4.2/5.0
Behavior3/5

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

With no annotations provided, the description carries the burden of disclosing behavior. 'List' implies a read-only operation, but there is no explicit statement about safety, ordering, or output format. It is sufficient but minimal.

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

Conciseness5/5

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

The description is a single sentence that efficiently states the tool's purpose and output without any wasted words.

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?

Given that the tool has no parameters and no output schema, the description adequately conveys what it does (list all sectors) and what it returns (startup counts). It could be slightly more descriptive about the output structure, but it is sufficient for such a simple tool.

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

Parameters4/5

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

The tool has zero parameters, so the baseline is 4. The description does not need to explain parameters, and the schema confirms there are none.

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 'List' with a clear resource ('tracked sectors') and adds a distinguishing detail ('with startup counts'), making it distinct from sibling tools like get_startup_signal, list_cities, and search_startups.

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 clearly implies when to use this tool: to retrieve all tracked sectors along with their startup counts. It provides clear context but does not explicitly mention alternatives or exclusionary conditions, 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.

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TDQS

A3.8/5.0
Disambiguation5/5

Each tool has a clear, distinct purpose: getting a signal for a specific startup, listing cities, listing sectors, and searching startups. There is no meaningful overlap between these operations.

Naming Consistency5/5

All tool names follow a consistent verb_noun snake_case pattern (get_startup_signal, list_cities, list_sectors, search_startups). The naming is predictable and uniform.

Tool Count5/5

With 4 tools, the server is tightly scoped to its purpose of exploring startup momentum data. Each tool is necessary and there is no bloat.

Completeness4/5

The core read-only workflow is covered: discover startups via search and get signal for a specific startup. Minor gaps like a detailed startup profile endpoint are absent but not critical for the apparent use case.

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