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Glama

Textcase

by io.github.pipeworx-io

Recall

recall
Read-onlyIdempotent

Retrieve a value previously saved via remember, or list all saved keys (omit the key argument). Use to look up context the agent stored earlier — the user's target ticker, an address, prior research notes — without re-deriving it from scratch. Scoped to your identifier (anonymous IP, BYO key hash, or account ID). Pair with remember to save, forget to delete.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
keyNoMemory key to retrieve (omit to list all keys)

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

A4.6/5.0
Behavior4/5

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

Annotations already declare safe read operations (readOnlyHint, idempotentHint). The description adds behaviors: listing keys on omission, and scoping to identifier. No contradictions.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is slightly long but well-structured, front-loading the main action and then providing context. 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?

No output schema exists, but the description explains what is returned (value or list). It covers pairing with remember/forget, scope, and usage context completely.

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% but the description adds meaning: omitting 'key' lists all keys. This clarifies the optional nature beyond the schema definition.

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 retrieves a value saved via remember or lists all keys when the argument is omitted. It explicitly distinguishes from siblings 'remember' 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?

It explains when to use (to look up stored context) and mentions alternatives: 'remember to save, forget to delete'. Also notes the scope (per identifier).

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

All Textcase toolsInspect
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TDQS

A3.6/5.0
Disambiguation2/5

Several tool clusters overlap heavily: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research all route questions to data sources; polymarket_arbitrage, polymarket_edges, polymarket_edge_tracker, polymarket_fill_risk, polymarket_kalshi_spread, and bet_research all target prediction-market analysis; and all_cases plus convert_case both handle text-case conversion. An agent will struggle to pick the right tool without deep reading.

Naming Consistency3/5

Nearly all names are lowercase with underscores, but the morphological pattern is mixed: some are verb_noun (convert_case, compare_entities, resolve_entity, scan_dependency), many are bare nouns (entity_profile, pipeworx_trending, polymarket_edges, all_cases), and a few are single-word verbs (forget, recall, remember). Still readable, but not a predictable verb_noun convention throughout.

Tool Count2/5

33 tools is far too many for a server named 'Textcase' — only all_cases and convert_case relate to the apparent purpose. The remaining 31 constitute a sprawling assortment of data research, prediction markets, memory, subscriptions, and feedback tools that have nothing to do with text casing, making the count feel bloated and misaligned.

Completeness2/5

For the stated text-case domain, the two converters cover the basic transformations but lack supporting operations like case detection, batch processing, or custom case definitions. More fundamentally, the tool surface is incoherent: the majority of tools serve foreign domains (Pipeworx data, Polymarket, subscriptions), so there is no clear domain to evaluate for completeness, and obvious gaps exist within whatever the server is meant to be.

Resources