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Glama

H1b

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)

TDQS

A4.9/5.0
Behavior5/5

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

Annotations indicate idempotentHint=true and destructiveHint=false. Description adds key details: key-value pair scoped by identifier, persistence differences (authenticated users get persistent memory, anonymous 24-hour retention). 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?

Front-loaded with primary action, then context, then scoping/persistence details. Every sentence is informative and concise.

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?

With 2 required parameters and no output schema, description fully covers purpose, usage, behavioral nuances, and pairing with siblings. No gaps.

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 has 100% coverage with descriptions. Description reinforces key-value pair concept and provides example key names ('subject_property'), adding marginal value beyond 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?

Description clearly states the tool saves data for reuse across conversations and sessions. It distinguishes from sibling tools like recall and forget by specifying the save action and pairing with retrieve/delete.

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?

Explicitly describes when to use: when discovering something worth carrying forward (ticker, address, preference). Pairs with recall and forget for retrieval and deletion, setting clear context.

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

A3.7/5.0
Disambiguation2/5

Several tool families have fuzzy boundaries: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research all route to the same 5,743 tools with only subtle differences in grounding/fan-out, and the six polymarket_* tools overlap heavily in purpose. Even with detailed descriptions, an agent would frequently need to read the full text to pick the right one, and the beta variant is admitted to be currently identical to the stable router.

Naming Consistency4/5

All tool names use snake_case with clear family prefixes (ask_pipeworx, polymarket_*, h1b_*, pipeworx_*, subscribe/unsubscribe), making the set look organized. The minor inconsistency is that some names start with an imperative verb (ask, compare, validate, scan) while others are bare nouns (entity_profile, deep_research, bet_research), so the verb_noun pattern is not universal.

Tool Count2/5

34 tools is well beyond the recommended range for an MCP server, and many are near-duplicates (three ask_pipeworx variants, six prediction-market analyzers, three memory/three subscription tools). The breadth may reflect a genuinely large data catalog, but exposing it all as top-level MCP tools makes the surface heavy and hard to navigate.

Completeness3/5

For the actual data-platform scope, coverage is solid: lookups, research, memory, subscriptions, and validation are all present. However, a direct fetch tool for the advertised pipeworx:// citation URIs is missing (deep_research even conditions citations on resources/read existing), and the server's stated H-1B identity is underrepresented with only three specialized tools.

Resources