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Glama

Cso Ie

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
Behavior4/5

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

Annotations already indicate idempotentHint=true and readOnly=false, so the description doesn't restate those. It adds useful context about key-value scoping by agent identifier and retention policies (persistent for authenticated users, 24 hours for anonymous), which is beyond the annotation signals. It doesn't explicitly mention overwrite behavior, but idempotency covers that.

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?

Three tightly worded sentences: the first states the core purpose, the second provides usage triggers, the third covers storage scope and companion tools. No redundant phrases or irrelevant details.

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 fully covers purpose, when-to-use, persistence behavior, and related tools. An agent can correctly select and invoke this tool without needing additional information.

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% and both parameters are self-explanatory. The description reinforces by giving key examples ('subject_property', 'target_ticker') and clarifying value is 'any text' (findings, addresses, preferences), which adds practical semantics beyond the schema field names.

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 starts with a specific verb and resource: 'Save data the agent will need to reuse later.' It clearly distinguishes this tool from its siblings by explicitly naming recall and forget as complementary operations, making its role in the memory trio 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?

It provides explicit guidance on when to use: 'Use when you discover something worth carrying forward' with concrete examples like resolved tickers and user preferences. It also contrasts authenticated vs anonymous sessions, helping the agent decide whether the memory will persist for the current 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

A3.8/5.0
Disambiguation3/5

Several tools have overlapping purposes: the four ask_pipeworx variants all route to the same 5,581 tools (ask_pipeworx_beta is currently identical to stable), and the six polymarket_* tools have subtle boundaries between research, edges, and arbitrage that could cause misselection. That said, the descriptions are unusually detailed, and non-overlapping clusters (CSO table tools, memory tools, subscription tools) are clearly distinct.

Naming Consistency3/5

All names are snake_case and mostly verb-first (get_dataset, resolve_entity, validate_claim), but conventions are inconsistent: polymarket_* and pipeworx_* are brand/noun-first while bet_research and ask_pipeworx put the verb first for the same domains, and some tools are pure nouns (entity_profile, recent_alerts). The pattern is readable but far from predictable.

Tool Count2/5

At 35 tools the server is well past the 25+ 'too many' threshold for a single MCP. Several tools don't earn their place: ask_pipeworx_beta is functionally identical to ask_pipeworx right now, and the six polymarket tools plus four ask_pipeworx variants represent heavy redundancy for what are essentially two sub-domains.

Completeness4/5

The data-access core is covered end to end: discovery (discover_tools, list_datasets, suggest_questions), structured reads (ask_pipeworx, get_dataset, query_dataset), grounded verification (validate_claim, ask_pipeworx_grounded), comparison (compare_entities), change tracking (recent_changes), plus memory and subscription CRUD. Minor gaps: subscriptions can't be edited (only recreated) and unrelated utilities (generate_llms_txt, scan_dependency) dilute the focus rather than fill a real gap.

Resources