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Glama

Data Orlando

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

A4.3/5.0
Behavior5/5

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

Adds significant context beyond annotations: persistence details (authenticated vs anonymous, 24-hour retention), scope by identifier, and the fact it's for reuse across sessions. 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.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Single paragraph that front-loads purpose. Efficient but could benefit from slight restructuring (e.g., bullet points for clarity). No unnecessary sentences.

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?

Given no output schema, description explains persistence, scope, and companion tools. Lacks details on overwrite behavior or limits, but sufficient for a simple save tool.

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?

Schema coverage is 100% with clear descriptions for key and value. The description reinforces 'key-value pair' but does not add new semantic detail beyond the schema's examples.

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' with specific verb (save) and resource (key-value pair). It distinguishes from siblings by name-dropping 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 Guidelines4/5

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

Explicitly says 'Use when you discover something worth carrying forward' and gives examples. Mentions pairing with recall and forget, but does not explicitly state when not to use it.

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

A3.9/5.0
Disambiguation3/5

Several clusters overlap in purpose: ask_pipeworx / ask_pipeworx_beta / ask_pipeworx_grounded / deep_research all answer factual questions, and the six Polymarket tools (arbitrage, edges, edge_tracker, fill_risk, kalshi_spread, bet_research) have subtle boundaries even with detailed descriptions. The descriptions are strong, but an agent must read carefully to reliably pick the right tool.

Naming Consistency4/5

Most tools follow a verb_noun snake_case pattern (ask_pipeworx, compare_entities, resolve_entity, validate_claim) with recognizable family prefixes (polymarket_*, pipeworx_*, ask_pipeworx_*). Minor deviations like single-noun names (datasets, metadata, query) and mixed lookup verbs (ask vs query vs search vs discover) keep it from a 5.

Tool Count2/5

34 tools is heavy for a single server, and the scope sprawls across general data routing, prediction-market analytics, memory management, subscription bookkeeping, AI visibility checks, and npm dependency scanning. The families are organized, but the count exceeds the 25-tool threshold and would be better split into focused servers.

Completeness4/5

For a data-research server, coverage is broad: universal routing, grounded answers, deep research, entity profiles, comparisons, change feeds, claim verification, dataset search/query/metadata, subscriptions, and memory. Minor gaps exist—no subscription update operation and no full-catalog browse beyond search—but agents can work around them.

Resources