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zenquotes

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. Added

TDQS

A4.3/5.0
Behavior4/5

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

Annotations already declare idempotentHint=true, destructiveHint=false, and readOnlyHint=false. The description adds useful behavioral context beyond these: key-value storage, scoping by identifier, and persistence details (authenticated vs 24-hour anonymous). It does not disclose overwrite behavior, but given annotation coverage, this is solid.

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 appropriately sized, front-loaded with the primary purpose, then usage, then behavioral specifics. Every sentence earns its place; no filler or redundancy. Examples are helpful and concise.

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 low-complexity tool (2 simple params, no output schema, no nested objects), the description covers purpose, when to use, storage model, persistence, and companion tools. It is complete enough for an agent to select and invoke correctly without further explanation.

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?

With 100% schema description coverage, the schema already fully explains key and value with examples. The description confirms a key-value pair and mentions scoping, but adds no additional parameter-level semantics beyond the schema, so the 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?

Description clearly states the tool's purpose: 'Save data the agent will need to reuse later' with a specific verb and resource. It distinguishes itself from sibling tools by explicitly mentioning recall (retrieve) and forget (delete), making the save-memory role unambiguous.

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 gives clear when-to-use guidance: 'Use when you discover something worth carrying forward' with concrete examples. It names companion tools (recall, forget) but does not explicitly state when not to use this tool or contrast it with alternatives beyond those companions, so it falls just short of a 5.

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

All zenquotes toolsInspect
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TDQS

A4/5.0
Disambiguation4/5

Most tools have clearly distinct purposes, and the heavy overlap between ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research is mitigated by detailed usage guidance. A few pairs like ai_visibility_check vs scan_competitor_ai_presence or discover_tools vs suggest_questions could confuse an agent, but the descriptions generally draw clear boundaries.

Naming Consistency3/5

Names are all lowercase snake_case and several families are consistent (ask_pipeworx*, polymarket_*), but the overall set mixes styles: verb_noun such as list_quotes and resolve_entity, adjectival noun phrases such as random_quote and today_quote, and bare nouns like entity_profile and recent_changes. The patterns are readable but not predictable.

Tool Count2/5

34 tools is well above the 25+ threshold for 'too many,' and the server name zenquotes suggests a much smaller quote-focused surface. The count is defensible for a broad data gateway, but as a unified server it feels overstuffed with many unrelated feature areas.

Completeness4/5

The combined surface is quite complete for its apparent scope: quote retrieval, query/research, grounding, entity resolution, comparisons, change feeds, subscriptions, memory, and prediction-market analysis are all covered. Minor gaps exist, such as no direct quote search and no explicit tool for fetching a pipeworx:// citation URI.

Resources