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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)

TDQS

A4.3/5.0
Behavior4/5

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

Annotations already signal idempotency and non-destructiveness. The description adds valuable context: key-value storage, scoping by identifier, and retention behavior (persistent for authenticated, 24 hours for anonymous). It does not mention overwrite behavior, but the idempotentHint covers the expectation that repeated saves are safe.

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 compact yet information-dense: purpose, usage trigger, storage model, retention policy, and companion tools are all covered in four sentences. No filler or redundancy. The most important action is front-loaded in the first sentence.

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, the description is complete. It explains why to use it, when to use it, how data is stored, how long it persists, and how to interact with it via recall/forget. No critical operational detail is missing.

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% and both key and value have clear descriptions with examples. The description adds the concept of key-value pairs and mentions scoping by identifier, but does not add meaningful parameter-level detail beyond what the schema already provides. Baseline 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 uses a specific verb ('Save') with a clear resource ('data the agent will need to reuse later'), and explains the cross-session scope. It also explicitly distinguishes this tool from siblings recall and forget by naming them as companions. This makes the tool's purpose unmistakable.

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?

It gives concrete when-to-use guidance with examples: 'Use when you discover something worth carrying forward...'. It names alternatives (recall, forget) and notes persistence differences for authenticated vs anonymous users. However, it does not explicitly state when not to use the tool or compare it to other storage-like tools.

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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Glama MCP Gateway

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TDQS

A3.6/5.0
Disambiguation2/5

Several tools have heavily overlapping purposes: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded differ only in mode, and the polymarket_* cluster (polymarket_edges, polymarket_edge_tracker, polymarket_arbitrage, polymarket_fill_risk, polymarket_kalshi_spread) all target prediction-market analysis with fuzzy boundaries. The presence of ai_visibility_check and scan_competitor_ai_presence, plus discover_tools and suggest_questions, adds further ambiguity about which tool to select first.

Naming Consistency3/5

Names are all snake_case but follow mixed conventions: verb_noun (list_feeds, read_feed, fetch_feed, validate_claim) coexists with noun_phrase (entity_profile, recent_changes, polymarket_edges) and prefix-grouped names (ask_pipeworx*, polymarket_*). While subgroups are internally consistent, the overall set lacks a unified pattern, though it remains readable.

Tool Count2/5

34 tools is well above the 25+ threshold for too many, and the count is especially inappropriate for a server named 'Sports Feeds' — most tools are generic data-research or meta-tools (subscriptions, memory, feedback, discovery) unrelated to sports feeds. The bloat suggests the server is actually a broad Pipeworx gateway, not a focused sports feeder.

Completeness2/5

For a sports-feeds server, only list_feeds, read_feed, and fetch_feed address the core domain, and there is no feed search, categorization beyond a simple list, or sports-specific analytics. While the general research surface (SEC, FDA, economics, prediction markets) is fairly comprehensive, it is misaligned with the stated server purpose, leaving the actual sports-feed functionality thin and with obvious gaps.