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Glama

Vies Eu

by io.github.pipeworx-io

Remember

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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

A4.5/5.0
Behavior4/5

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

Annotations already indicate the tool is non-read-only, non-destructive, and idempotent. The description adds valuable behavioral context: scoping by identifier, persistence differences between authenticated users (persistent) and anonymous sessions (24 hours), and the key-value store structure. This goes beyond the annotations without contradicting them.

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?

The description is concise and well-structured, front-loading the core purpose in the first sentence. It includes only relevant usage details, persistence behavior, and related tools, with no redundant or filler content. Every sentence contributes meaningfully.

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 tool with two string parameters and no output schema, the description thoroughly covers purpose, usage scenarios, scoping, persistence, and relationship to the memory tool family. It provides sufficient context for the agent to select and invoke the tool correctly without needing external documentation.

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?

The input schema already provides 100% coverage for both parameters with clear descriptions and examples. The tool description adds minimal extra semantic value, only repeating the key-value pair concept already evident from the schema. Baseline 3 is appropriate since the schema does the heavy lifting.

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's purpose with a specific verb ('Save data') and resource ('data the agent will need to reuse later'). It also distinguishes itself from sibling tools by explicitly naming recall and forget as complementary counterparts, making the role unambiguous.

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?

The description provides explicit guidance on when to use the tool ('Use when you discover something worth carrying forward') with concrete examples. It also mentions the companion tools recall and forget, giving clear alternatives and context for when to use each.

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

A3.8/5.0
Disambiguation2/5

Multiple tools have heavily overlapping purposes: ask_pipeworx, ask_pipeworx_beta (explicitly identical to the stable router right now), ask_pipeworx_grounded, and deep_research all route the same class of questions, making mis-selection easy. The six Polymarket tools (bet_research, polymarket_arbitrage, polymarket_edges, polymarket_edge_tracker, polymarket_fill_risk, polymarket_kalshi_spread) also blur together around edge detection and arbitrage, further muddying tool boundaries.

Naming Consistency4/5

All 33 tools use consistent lowercase snake_case naming, and most follow a clear verb_noun pattern (check_vat, compare_entities, resolve_entity, unsubscribe). A handful of noun-style names (entity_profile, bet_research, polymarket_edges, recent_alerts) deviate from the verb-first pattern but are still predictable and readable.

Tool Count2/5

33 tools is beyond the 25+ threshold for a coherent set, and the scope is sprawling: universal data routing, prediction-market analytics, VAT validation, AI visibility, memory, subscriptions, npm dependency scanning, and feedback. While each sub-domain has reason to exist, bundling them all into one server creates a kitchen-sink feel with too many entry points.

Completeness3/5

The core data-routing domain is well covered: universal router, grounded mode, deep research, entity resolution, profiles, comparisons, change feeds, claim validation, and search-within. VAT has check + status, and memory/subscription lifecycles are complete. However, there is no standalone tool to fetch a raw pipeworx:// record that citations reference, and the extreme breadth means no single domain is exhaustively fleshed out.

Resources