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Phoenix Number

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

A4.9/5.0
Behavior5/5

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

Annotations provide idempotentHint=true, destructiveHint=false, readOnlyHint=false. The description adds detail: data is scoped by identifier, authenticated users get persistent memory, anonymous sessions retain for 24 hours. It confirms that this is a write operation (not read-only) and non-destructive. 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.

Conciseness5/5

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

The description is a single focused paragraph that front-loads the purpose immediately. Every sentence adds value: examples, usage scenario, persistence details, and pairing with other tools. No fluff or repetition.

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 no output schema, the description adequately covers what the tool does, when to use it, how it behaves, and how it fits with sibling tools. It explains persistence differences and pairing with recall/forget, making the agent fully informed.

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% with clear descriptions for key and value. The description adds context with examples (key: 'subject_property', value: 'any text') and explains the purpose of each parameter, reinforcing schema details and providing usage guidance.

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: 'Save data the agent will need to reuse later' and provides concrete examples (resolved ticker, target address, user preference). It distinguishes from siblings like recall and forget by specifying that this is for storing, not retrieving or deleting.

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?

Explicit guidance on when to use: 'Use when you discover something worth carrying forward... so you don't have to look it up again.' It also mentions pairing with recall and forget, giving a complete workflow 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

A3.5/5.0
Disambiguation2/5

Many tools overlap heavily: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research all route questions to the same underlying sources; bet_research, polymarket_edges, and polymarket_arbitrage all find betting opportunities. entity_profile, recent_changes, and compare_entities similarly overlap on company data. The set includes near-duplicates, making misselection likely.

Naming Consistency3/5

All names use snake_case and lowercase, but the verb-noun pattern is inconsistent: some are verb-first (discover_tools, validate_claim), others noun-first (entity_profile, bet_research, pipeworx_trending), and some are noun-noun (polymarket_arbitrage). Prefixes like ask_pipeworx and polymarket_ provide some consistency, but overall naming style is mixed yet readable.

Tool Count2/5

With 32 tools, this is above the comfortable range for a coherent server. The count is bloated by three near-identical ask_pipeworx variants, six polymarket tools, and several overlapping meta-tools. A smaller, more focused set would be more appropriate.

Completeness3/5

As a data-access platform, it covers lookup, grounded answers, deep research, entity comparison, validation, and discovery, plus memory and subscription lifecycle. However, the set is a grab-bag with no unified purpose; there are dead ends like no way to directly call the 5,354 underlying tools except through ask_pipeworx, and the joke tool adds nothing to any workflow. The heterogeneous scope makes it hard to assess true completeness.

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