Skip to main content
Glama

Intact

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)

TDQS

A4.9/5.0
Behavior5/5

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

Discloses scope (key-value per identifier), persistence (persistent for authenticated, 24h for anonymous), and aligns with annotations (write, idempotent, non-destructive). Adds value beyond 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?

Four well-structured sentences, front-loaded with purpose, no redundant information—every 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?

Covers all aspects: what, when, how (key-value), scope, persistence, and companion tools. No gaps given the tool's simplicity.

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 examples; description adds real-world context (e.g., 'user_preference') and scenarios, enhancing understanding despite full schema coverage.

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 the tool saves data for reuse across conversations/sessions, with specific examples like 'resolved ticker, target address'. It distinguishes from siblings like 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 says 'Use when you discover something worth carrying forward' and provides concrete scenarios. It also directs to pair with recall/forget for lifecycle management.

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

All Intact toolsInspect
Try in Browser
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

A4.1/5.0
Disambiguation3/5

Most tools have strong, detailed descriptions with explicit usage guidance, but a few clusters are genuinely ambiguous: ask_pipeworx_beta is currently an exact duplicate of ask_pipeworx, and the Polymarket tools (bet_research, polymarket_edges, polymarket_arbitrage, polymarket_edge_tracker) overlap heavily in purpose. The descriptions help differentiate them, but an agent could still easily select the wrong variant.

Naming Consistency4/5

All tool names are snake_case and most follow an imperative verb-first pattern such as resolve_entity, subscribe, or validate_claim. A few noun-style names like entity_profile, bet_research, and interaction_count deviate, but the consistent underscore style and clear prefixes like polymarket_ and pipeworx_ keep the set predictable.

Tool Count2/5

At 33 top-level tools, the surface is heavy, and several tools are near-duplicates or narrow variants of the same core capability. The broad scope explains some of the count, but the agent-facing API would be cleaner with fewer, more consolidated entry points.

Completeness5/5

The set provides strong lifecycle coverage for its main workflows: querying and grounding, deep research, entity resolution, company profiling, comparisons, Polymarket edge analysis with fill-risk checks, memory storage, and subscription management. There are no obvious dead ends that would prevent an agent from completing a typical task.

Resources