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Glama

genderize

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)

Schema Changelog

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

  1. Added

TDQS

A4.7/5.0
Behavior5/5

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

Beyond the annotations (idempotentHint, destructiveHint), the description discloses additional behavioral context: scoping by identifier, persistence differences between authenticated and anonymous sessions (24-hour retention), and the storage format. This gives the agent critical operational expectations not captured in 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 four focused sentences, front-loaded with the primary purpose, followed by usage, storage details, and tool pairing. No verbose or redundant language; 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?

For a simple two-parameter write tool with no output schema and limited annotations, the description covers purpose, usage, persistence semantics, and relationships to related tools. It fully equips the agent to decide when and how to call it.

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?

Schema coverage is 100% with detailed descriptions for both 'key' and 'value'. The tool description adds usage examples but does not meaningfully change param semantics. Since the schema already carries the load, a baseline of 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 opens with a clear action and target: 'Save data the agent will need to reuse later' combined with the storage model of 'key-value pair'. It directly differentiates from siblings by explicitly pairing with 'recall to retrieve later, forget to delete', which clarifies its role as the write operation in the memory trio.

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?

Usage guidance is explicit: 'Use when you discover something worth carrying forward' with concrete examples (resolved ticker, target address, user preference). It also names complementary tools (recall, forget), effectively communicating when to use this tool versus its siblings.

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

All genderize toolsInspect
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TDQS

A3.8/5.0
Disambiguation2/5

Several tools route the same style of query to the same Pipeworx catalog: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, discover_tools, and suggest_questions all overlap in purpose, and ask_pipeworx_beta is explicitly identical to ask_pipeworx. The prediction-market tools also blur together, with bet_research, polymarket_arbitrage, polymarket_edges, polymarket_edge_tracker, and polymarket_fill_risk all covering overlapping analysis territory.

Naming Consistency3/5

The tools are consistently lowercase snake_case, but the naming convention is mixed: some are verb_noun (predict_gender, generate_llms_txt), some are noun phrases (entity_profile, recent_alerts), some are bare verbs (remember, forget), and many share domain prefixes like ask_pipeworx or polymarket_. It is readable, but there is no single predictable pattern across the set.

Tool Count2/5

At 33 tools, this exceeds the 25+ threshold where the surface becomes hard to navigate. More importantly, the count does not match the server's apparent genderize identity: the vast majority of tools are unrelated Pipeworx research, prediction-market, memory, and subscription utilities bolted onto a two-tool gender-prediction core.

Completeness3/5

As a broad research assistant, the set is substantial: it covers question routing, grounded verification, deep research, entity profiles, comparisons, change feeds, memory, and subscriptions. However, the actual genderize domain is thin—just two prediction tools with no batch, supported-country, or accuracy endpoints—and several unrelated capabilities feel bolted on, making coverage uneven.

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