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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

A4.3/5.0
Behavior4/5

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

The description adds valuable behavioral context beyond annotations: it explains scoping ('scoped by your identifier') and persistence semantics ('persistent memory' vs 'anonymous sessions retain memory for 24 hours'). These details are not covered by idempotentHint or destructiveHint and meaningfully inform the agent about side effects and storage duration.

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 three sentences long, each earning its place: action, usage context, and persistence details. It is front-loaded with the core purpose and avoids redundant phrasing, making it concise and well-structured.

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 2-parameter tool with no output schema, the description is complete. It covers purpose, usage, persistence, scoping, and complementary tools. Annotations already provide idempotence/destruction hints. No additional behavioral information is needed for an agent to use the tool correctly.

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 description coverage is 100% for both key and value parameters, so the schema fully documents them. The tool description mentions key-value pairs and gives examples of stored content ('resolved ticker, target address'), but this is illustrative rather than adding new syntax or format details. Baseline 3 is appropriate given high schema coverage.

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 function: 'Save data the agent will need to reuse later' – a specific verb ('save') and resource ('data'). It distinguishes itself from siblings by explicitly mentioning 'recall' for retrieval and 'forget' for deletion, making the purpose unique among similar memory tools.

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 provides clear when-to-use guidance: 'Use when you discover something worth carrying forward... so you don't have to look it up again.' It also names complementary tools (recall, forget). However, it doesn't explicitly state when not to use it, though the phrase 'worth carrying forward' implies the condition.

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

A3.9/5.0
Disambiguation2/5

Several tools overlap heavily: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research all route questions to the same 5,529-tool catalog, with ask_pipeworx_beta currently identical to ask_pipeworx. The Polymarket suite also has ambiguous boundaries (bet_research vs. polymarket_edges vs. polymarket_arbitrage), and scan_competitor_ai_presence simply wraps ai_visibility_check, so agents may struggle to choose the right tool.

Naming Consistency3/5

All names are snake_case and readable, but the set mixes verb-led names (ask_pipeworx, compare_entities, scan_dependency, validate_claim, hts_search) with noun-led names (entity_profile, recent_alerts, polymarket_edges, ai_visibility_check). Variant suffixes like ask_pipeworx_beta / ask_pipeworx_grounded add further inconsistency, so the naming is coherent enough but not predictable enough for a 4.

Tool Count2/5

At 33 tools, the server exceeds the 25-tool threshold for 'too many' in the rubric. Even though the broad data-research scope justifies a larger surface, the count feels bloated because several tools are near-duplicates (e.g., ask_pipeworx_beta, ask_pipeworx_grounded) or wrappers (scan_competitor_ai_presence), making the set heavier than necessary.

Completeness4/5

The server covers its core domains thoroughly: data querying (ask_pipeworx family, deep_research), entity resolution and comparison (resolve_entity, entity_profile, compare_entities), verification (validate_claim, ask_pipeworx_grounded), HTS tariff lookup (search + detail), subscription lifecycle (subscribe/unsubscribe/list/recent_alerts), and memory (remember/recall/forget). Minor gaps exist, such as no subscription-update endpoint and no generic raw-source export, but these are workaround-able and do not cause agent failures.