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Glama

Get Pack Tools

get_pack_tools
Read-onlyIdempotent

Get tool definitions for a specific pack (e.g., 'weather', 'polygon-io'). Returns tool names, descriptions, parameters, and requirements. Use before calling a tool to verify its interface.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
slugYesPack slug (e.g., weather, pokemon, github)

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
slugYesPack slug
toolsYesList of tools in the pack
connectYesConnection instructions
tool_countYesNumber of tools in the pack
gateway_urlYesMCP gateway URL for the pack

TDQS

A4.3/5.0
Behavior4/5

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

Annotations already mark the operation as readOnly, idempotent, and non-destructive. The description adds useful behavioral context by listing the returned content (names, descriptions, parameters, requirements) and by framing the tool as a pre-call verification step. There is no contradiction with annotations.

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 two sentences with no fluff. It starts with the core action, provides examples and output details, and ends with the practical use case. Every sentence earns its place.

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

Completeness5/5

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

For a single-parameter lookup tool, the description covers what it does, what it returns, and when to use it. The annotations and schema cover safety and parameter details, and an output schema exists, so nothing critical is missing.

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?

The schema fully documents the single 'slug' parameter with description and examples, so the description does not need to add much. It does reinforce the parameter's meaning by naming example packs, but no additional semantic depth is provided.

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?

Even though siblings exist, the description clearly identifies the function as retrieving tool definitions for a named pack, making the purpose unambiguous.

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?

It explicitly says 'Use before calling a tool to verify its interface,' which tells the agent when to invoke it. It does not name alternatives or exclusion cases, but the context is clear enough for a lookup tool.

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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Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

A4/5.0
Disambiguation4/5

Most tools have clearly distinct purposes, especially the central data access tools like ask_pipeworx, deep_research, entity_profile, and compare_entities. However, the multiple polymarket tools (arbitrage, edges, edge_tracker, fill_risk, kalshi_spread) and the similar ask_pipeworx variants could cause confusion, especially for an agent quickly scanning options.

Naming Consistency4/5

Tool names are mostly snake_case and follow a verb_noun pattern (e.g., compare_entities, search_packs, resolve_entity). Some deviations exist, such as pipeworx_feedback, polymarket_arbitrage (starting with a noun), and single-word names like forget and remember, but overall the style is readable and consistent.

Tool Count2/5

With 36 tools, the server feels overly heavy. While the broad domain (structured data across many sources) justifies a large number, the count exceeds the recommended 15–25 range, making it unwieldy for agents to navigate efficiently without extensive discovery.

Completeness4/5

The tool set covers a wide range of domains: company financials, drugs, economics, prediction markets, weather, and even MCP discovery. There are few obvious gaps given the stated purpose, though some areas like social media or international data could be added. Overall, the surface is well-rounded.