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Glama

Data Santaclara

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

A4.8/5.0
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: persistence differences (authenticated vs anonymous), 24-hour retention for anonymous, and key-value pair scoping. 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?

Two sentences with front-loaded purpose, then usage, then behavioral details. Every sentence earns its place with no redundancy.

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?

Covers purpose, usage, persistence, and pairing with recall/forget. No output schema exists, but for a storage tool the description is sufficient. Minor gap: doesn't mention if overwriting existing key is possible or any size limits.

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%, but description adds value by providing concrete examples for key (e.g., 'subject_property', 'target_ticker') and clarifying that value is 'any text.' This aids correct usage beyond bare 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 'Save data the agent will need to reuse later' and provides specific examples like ticker, address, preference. It distinguishes from sibling tools recall and forget by explaining its role in the workflow.

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 states 'Use when you discover something worth carrying forward' and 'Pair with recall to retrieve later, forget to delete.' It gives clear context for when and how to use this tool.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All Data Santaclara toolsInspect
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TDQS

A3.9/5.0
Disambiguation2/5

Multiple tools overlap heavily: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, and validate_claim all route questions to the same 5,798-tool catalog and can return similar evidence-backed answers. Additionally, polymarket_arbitrage, polymarket_edges, polymarket_fill_risk, polymarket_edge_tracker, and bet_research all circle prediction-market edge detection, creating boundary ambiguity despite detailed descriptions.

Naming Consistency3/5

Most tools follow a verb_noun or noun_verb pattern (e.g., list_subscriptions, generate_llms_txt, scan_dependency, compare_entities, resolve_entity), and consistent snake_case is used throughout. However, some names are vague and unclear (query, metadata, recall, forget, datasets), and the ask_pipeworx family is not clearly versioned in naming.

Tool Count2/5

34 tools is heavy for a server that is conceptually a data-access gateway plus a few meta utilities. The count is inflated by multiple near-duplicate research modes (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research) and an extensive prediction-market subfamily that could be consolidated.

Completeness4/5

The server covers its main domains well: entity resolution, company profiles, comparisons, change feeds, claim verification, grounded Q&A, and data discovery all exist. Minor gaps include no obvious tool for general web search or full-text legal records, and the subscription/alert system lacks an update-subscription tool, but core workflows have no dead ends.

Resources