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Glama

Devdocs Io

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

A4.9/5.0
Behavior5/5

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

Adds substantial context beyond annotations: key-value scoping by identifier, persistence duration for authenticated vs anonymous sessions. Aligns with idempotentHint and destructiveHint, no contradictions.

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 sentences, front-loaded with the core action, and every sentence adds value—purpose, usage, storage details, and related tools. No waste.

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 simple two-parameter tool with no output schema, the description covers purpose, usage triggers, storage semantics, persistence, and companion tools. Fully sufficient for an agent to select and invoke correctly.

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 covers 100% of parameters, and description adds examples of valid keys ('subject_property', 'target_ticker') and values. It doesn't fully explain edge cases but enriches the schema meaning.

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 later reuse, with specific examples of what to keep. It distinguishes itself from sibling tools recall and forget by explicitly mentioning pairing with them.

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?

It provides explicit when-to-use guidance with examples ('resolved ticker, target address') and tells the agent to pair with recall for retrieval and forget for deletion, covering alternatives.

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

B3.1/5.0
Disambiguation3/5

There are many tools with overlapping purposes (e.g., multiple ways to ask questions, multiple Polymarket analysis tools, multiple company lookup tools). The detailed descriptions help distinguish them, but an agent could still easily select the wrong one.

Naming Consistency2/5

Tool names are inconsistent, mixing verb_noun patterns (ask_pipeworx, search_docs) with single words (db, docs) and compound names (polymarket_arbitrage, ai_visibility_check). No clear convention across the set.

Tool Count2/5

37 tools is excessive for a DevDocs documentation server; most tools are unrelated to documentation (Pipeworx data, memory, subscriptions). The core documentation functionality only requires about 7-8 tools, making the rest feel extraneous.

Completeness3/5

For the core DevDocs functionality, the tools cover listing, searching, and fetching documentation. However, the server includes many unrelated tools that are incomplete on their own (e.g., only some data lookups, no CRUD for prediction markets). Thus overall completeness is mediocre.

Resources