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Glama

Figma

by io.github.pipeworx-io

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Idempotent

Save data the agent will need to reuse later — across this conversation or across sessions. Use when you discover something worth carrying forward (a resolved ticker, a target address, a user preference, a research subject) so you don't have to look it up again. Stored as a key-value pair scoped by your identifier. Authenticated users get persistent memory; anonymous sessions retain memory for 24 hours. Pair with recall to retrieve later, forget to delete.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
keyYesMemory key (e.g., "subject_property", "target_ticker", "user_preference")
valueYesValue to store (any text — findings, addresses, preferences, notes)

Schema Changelog

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

  1. First observed

TDQS

A4.6/5.0
Behavior4/5

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

The description adds important behavioral context beyond annotations: scoping by identifier, 24-hour retention for anonymous sessions, and persistent memory for authenticated users. Annotations already indicate idempotent and non-destructive, and description aligns. Minor gap: no mention of overwriting behavior on duplicate keys, but overall strong.

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?

Four concise sentences, each meaningful. The core purpose is front-loaded, and every sentence adds value—usage guidance, examples, scope, retention, and pairing. No waste.

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 2-param tool with no output schema, the description is quite complete. It covers purpose, when to use, pairing, scoping, and retention. It does not mention error cases or return value, but those are minor given the simplicity.

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?

Schema coverage is 100% with clear param descriptions. The description reinforces key-value pair concept and gives typical key examples, adding value beyond the schema.

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 clearly states the tool saves data for reuse, with specific examples like 'resolved ticker' and 'user preference'. It distinguishes itself from sibling tools by referencing 'recall' and 'forget'.

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

Usage Guidelines5/5

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

The description explicitly guides when to use the tool ('when you discover something worth carrying forward'), provides concrete examples, and mentions pairing with related tools (recall, forget). It also clarifies persistence behavior for different authentication states.

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

A3.5/5.0
Disambiguation2/5

Many tools have overlapping purposes (e.g., ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded), and the set mixes Figma-specific tools with a large library of data-lookup tools, making it hard for an agent to distinguish when to use which.

Naming Consistency2/5

Naming conventions are inconsistent: snake_case (ask_pipeworx, deep_research), camelCase (get_me, get_file), and mixed underscores (list_comments). There is no consistent pattern across the tool set.

Tool Count1/5

The server is named 'Figma' but has 36 tools, only 5 of which are Figma-related. The remaining 31 are Pipeworx data tools, indicating a severe scope mismatch and unnecessary bloat for a Figma server.

Completeness1/5

The Figma-specific tools lack essential CRUD operations (e.g., no create, update, or delete for files or nodes). The Pipeworx tools, while extensive, are irrelevant to Figma, so the server is severely incomplete for its stated purpose.

Resources