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Glama

Markdown

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

A4.5/5.0
Behavior5/5

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

Beyond the annotations (readOnlyHint=false, idempotentHint=true), the description adds significant behavioral context: storage is 'scoped by your identifier', authenticated users get persistent memory, anonymous sessions retain for 24 hours. This discloses persistence and scoping traits not implied by 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?

The description is three sentences, front-loaded with purpose, then usage, then storage details. Every sentence contributes new information without fluff or 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?

Given the simple 2-parameter schema, present annotations, and no output schema, the description fully covers purpose, usage, persistence, and sibling integration. It is complete for an agent to select and invoke the tool correctly.

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 full descriptions and examples for 'key' and 'value' (100% coverage). The description adds no per-parameter specifics beyond what the schema offers, though it reinforces the key-value pair concept. Baseline 3 is appropriate.

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 function: 'Save data the agent will need to reuse later' with a specific verb ('Save') and resource ('data'). It distinguishes from siblings by explicitly pairing with 'recall' and 'forget' for related operations, making its role unique.

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?

The description provides explicit when-to-use guidance: 'Use when you discover something worth carrying forward' with concrete examples (ticker, address, preference). It names complementary tools ('Pair with recall... forget to delete') but does not explicitly state when not to use it, so it falls short of the full 5 criterion.

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

Several tool clusters are hard to distinguish: the four ask_pipeworx variants overlap heavily (beta currently behaves identically to ask_pipeworx, and grounded shares routing), and the half-dozen prediction-market tools (bet_research, polymarket_edges, polymarket_arbitrage, polymarket_edge_tracker, polymarket_fill_risk, polymarket_kalshi_spread) have fuzzy boundaries. Memory and markdown utilities are clear, but the overlapping data-query and prediction-market clusters create real misselection risk.

Naming Consistency4/5

Names are almost uniformly snake_case and mostly verb-first (ask_, compare_, extract_, scan_, validate_, remember, forget), which is predictable. Minor deviations like entity_profile, recent_changes, and polymarket_edges are noun-first but still follow the same lowercase_snake pattern, so no chaotic mixing of conventions exists.

Tool Count2/5

34 tools is heavy for a server seemingly named 'Markdown', especially when the majority are actually Pipeworx data-research and prediction-market tools. Several tools duplicate or wrap each other (ask_pipeworx_beta, scan_competitor_ai_presence, bet_research et al.), so the count feels bloated; it is not as extreme as 50+, but it exceeds a well-scoped set.

Completeness4/5

Within the actual dominant domain — structured data research plus prediction markets — the surface is broad: routing queries, grounded answers, deep research, entity profiles, comparisons, resolution, claim validation, semantic search, discoverability, subscriptions/alerts, and memory are all covered. Minor gaps exist (e.g. no direct pipeworx:// citation-fetch tool, and markdown support is thin), but the core workflows have no dead ends.

Resources