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Glama

Exa

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

A4.7/5.0
Behavior5/5

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

Adds details beyond annotations: key-value pair scoped by identifier, 24-hour retention for anonymous, persistent for authenticated. Annotations only provide idempotentHint=true and non-destructive. 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 concise sentences, front-loaded with core purpose, no extraneous wording. Each sentence adds value.

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 storage semantics, scoping, and retention. Sufficient for an agent to use 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 coverage is 100%, but description provides example keys (subject_property, target_ticker) and clarifies value as 'any text'. This adds contextual meaning beyond the schema's generic descriptions.

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 'Save data the agent will need to reuse later' – a specific verb and resource. It distinguishes from siblings like recall and forget by explicitly mentioning them.

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?

Describes when to use: 'when you discover something worth carrying forward'. Also explains persistence differences for authenticated vs anonymous users. Lacks explicit when-not-to-use, but the 'use when' guidance is clear.

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 tools share the same basic purpose: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research all route questions to data sources, and ask_pipeworx_beta is currently identical to ask_pipeworx. The polymarket_* family has five overlapping tools (edges, arbitrage, edge_tracker, fill_risk, kalshi_spread), though detailed descriptions and explicit 'use when' guidance help separate them. Overall, an agent can generally pick the right tool but faces real ambiguity in the query-router and betting clusters.

Naming Consistency4/5

Most tools follow a clear verb_noun snake_case convention (search, get_contents, resolve_entity, validate_claim, subscribe, unsubscribe). However, several noun-first names break the pattern: entity_profile, ai_visibility_check, pipeworx_feedback, pipeworx_trending, and the polymarket_* family, plus adjective-noun names like recent_alerts and recent_changes. The deviations are readable and mostly clustered around product-specific domains, so the inconsistency is minor.

Tool Count2/5

At 34 tools, this significantly exceeds the 25+ threshold for a heavy tool surface. The server bundles four distinct domains — web search, structured data routing, prediction-market analysis, and memory/subscriptions — into one MCP endpoint, which inflates the count. While each domain has some justification, a more focused split into separate servers would yield better coherence.

Completeness4/5

The surface is remarkably thorough for its blended scope: search has query/retrieve/similar/within, structured data has default/grounded/beta/deep-research modes, subscriptions have full lifecycle coverage, and memory has save/recall/delete. Minor gaps exist, such as no subscription-update tool and no direct pipeworx:// URI reader in the tool list, but these are workable. The prediction-market and entity-analysis workflows are covered end to end.

Resources