Validate a JSON document and return errors. Use POST with JSON body. Use for data-pipeline agents.
Example call: {"body": "{"a":1}"}
Cost: $0.005–$0.05 USDC on Base per call.
| Name | Required | Description | Default |
|---|---|---|---|
| query_string | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description reveals that the tool uses POST with a JSON body and provides cost information. However, it contradicts the input schema, which only contains a 'query_string' parameter, not a body. This inconsistency undermines transparency about expected inputs.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is relatively concise with two sentences, an example, and a cost line. However, the example is inconsistent with the schema, detracting from clarity. The structure could be improved by aligning the example with the actual parameter.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the simple validation function with 0% schema coverage and no annotations, the description should fully explain usage. It covers purpose and cost but fails to clarify the parameter and output, leaving gaps for an agent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has one parameter, 'query_string', with 0% schema description coverage. The description does not explain this parameter and instead shows an example with a 'body' field, which is not present in the schema. This is misleading and fails to add meaning.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states 'Validate a JSON document and return errors' with a specific verb and resource. It also specifies the HTTP method and target audience, making the tool's purpose unambiguous and distinct from numerous sibling lookup tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The tool is explicitly intended for 'data-pipeline agents,' providing clear context. However, it does not mention when not to use it or compare it with alternatives, though no direct alternative exists among siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
The set is riddled with near-duplicates: lookup_reddit/scrape_reddit, lookup_wikipedia/scrape_wikipedia, lookup_dockerhub/scrape_dockerhub, lookup_steam/scrape_steam, enrich_googlereviews/enrich_reviews, lookup_ip/lookup_ipinfo, and multiple crypto-pricing tools (lookup_crypto, lookup_coingecko, bundle_crypto_360, scrape_binance, scrape_coinbase). Descriptions try to differentiate with phrases like 'heavier than' or 'same domain but with full thread parsing,' but the boundaries are fuzzy and an agent can easily pick the wrong one.
Naming follows a fairly consistent prefix-based snake_case pattern (lookup_, scrape_, enrich_, bundle_, search_, ai_, data_) where the prefix denotes action weight and the noun identifies the target. Minor deviations exist: posts_x, ai_ask/pro/ultra (model-tier names instead of resources), sslstatus (missing underscore), and lookup_useragents_top are slightly off-pattern.
172 tools is an extreme count, far beyond even the 50+ floor for a score of 1. This floods the agent's context and tool-selection space, making every call require a search through a massive list. While aggregation servers can justify more tools, this volume is unmanageable and every tool must be evaluated by the agent.
The surface is extraordinarily broad but unevenly deep: many sources have both a light lookup and a heavy scrape variant, while other areas have just a single shallow endpoint. There is no coherent domain with complete lifecycle coverage, and despite the huge catalog, common capabilities are still absent. The breadth prevents obvious gaps, but depth and coherence suffer.