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Glama

Regrid

by io.github.pipeworx-io

Remember

remember
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 important context beyond annotations: scoping by identifier, persistence differences for authenticated vs anonymous users (24-hour retention), key-value nature. No contradiction with annotations.

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?

Single paragraph with front-loaded purpose, followed by use case, scope, and pairing. Every sentence adds value, no redundancy.

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 2-param tool with no output schema, the description fully covers purpose, usage, behavior, and context. 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 covers both parameters with descriptions (100% coverage). Description adds example keys and purpose context, but does not significantly extend schema details; baseline 3 with modest addition.

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?

Clear verb ('save') and resource ('data to reuse later') with specific scope ('across conversation or sessions'). Differentiates from siblings by mentioning '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 Guidelines5/5

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

Explicitly states when to use ('when you discover something worth carrying forward') and gives concrete examples. Pairs with recall/forget, providing clear alternative 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
Disambiguation3/5

Most tools have detailed guidance, but several sets blur together: ask_pipeworx_beta is currently identical to ask_pipeworx, polymarket_edges and polymarket_arbitrage both scan for opportunities, and discover_tools/suggest_questions both serve discovery. The descriptions are strong enough to prevent frequent misselection, but the boundaries are not always crisp.

Naming Consistency3/5

Names are consistently snake_case, but the stylistic pattern is mixed: verb_noun names like generate_llms_txt and list_subscriptions sit alongside bare verbs like remember/forget and noun-phrase names like entity_profile, recent_alerts, and polymarket_arbitrage. Prefixes like polymarket_*, pipeworx_*, and regrid_parcel_* add some order, but the set is not uniform.

Tool Count2/5

33 tools is well above the point where a tool set remains easy to navigate, and several tools are near-duplicates or wrappers: ask_pipeworx_beta duplicates ask_pipeworx, scan_competitor_ai_presence is a wrapper around ai_visibility_check, and the prediction-market scanners overlap. The broad domain explains some of the bulk, but the surface still feels overweight.

Completeness3/5

The server covers a wide range of workflows: lookup, deep research, claim validation, entity profiles, comparisons, subscriptions, memory, prediction-market analysis, and parcel lookup. However, the Regrid parcel side is thin with only address and point lookup, and there is no direct tool for parcel-ID/owner/sales/tax queries. These are real gaps, though the universal router helps agents work around them.

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