Text summarizer (Workers AI) — Summarize arbitrary text into a concise summary. POST {text, sentences?}. Runs Llama on Workers AI. JSON. Price: $0.008 USDC (Base, via x402).
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes | text to summarize | |
| sentences | No | optional target sentence count (default 3) |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and destructiveHint=false. The description adds meaningful behavioral details beyond that: it uses POST, runs Llama on Workers AI, returns JSON, and crucially states a cost of $0.008 USDC via x402. This pricing information is important for an agent deciding whether to invoke the tool, though it does not cover rate limits or failure behavior.
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 compact and front-loaded with purpose, followed by protocol, model, output format, and price. It wastes little space, though 'Workers AI' appears twice ('Text summarizer (Workers AI)' and 'Runs Llama on Workers AI),' creating a minor redundancy that prevents a perfect score.
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?
For a simple read-only summarizer with 2 well-documented parameters and no output schema, the description covers purpose, parameters, endpoint, model, response format, and cost. It does not specify the exact JSON response structure, but the purpose clause 'Summarize arbitrary text into a concise summary' implies what the JSON contains, so the agent can likely use it correctly.
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?
Schema description coverage is 100%, so both parameters (text and sentences) are already documented in the schema. The description's 'POST {text, sentences?}' merely echoes the parameter names without adding new semantic meaning beyond what the schema provides. Baseline 3 is appropriate.
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 the verb and resource: 'Summarize arbitrary text into a concise summary.' It signals breadth with 'arbitrary text,' which loosely distinguishes it from domain-specific siblings like wiki_summary, but it does not explicitly name or contrast any sibling tool, so it falls short of a 5.
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?
Usage is implied through the stated purpose: use it when you need to summarize arbitrary text. However, there is no explicit guidance on when NOT to use it or which alternative tool to choose instead (e.g., ai_chat or wiki_summary), leaving the agent to infer boundaries from the sibling list.
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.
Many tools are clearly separated by prefix and data source, but several bundled products overlap heavily: vehicle_deal_check vs vehicle_report, realestate_property_report vs realestate_site_risk, finance_company_360 vs finance_health_scan, and domain_due_diligence vs email_domain_check/business_vet. An agent would frequently struggle to pick the correct premium bundle.
Tool names overwhelmingly follow a consistent snake_case category-prefix pattern like weather_, crypto_, vehicle_, finance_, and geo_. Minor deviations such as bare names (domain, ip) and noun-verb forms (dns_lookup, url_check) are easy to learn and don't create real confusion.
50 tools is far beyond the typical well-scoped 3–15 range and will require heavy filtering to navigate. The broad multi-domain data marketplace partially justifies the size, but it would be more coherent split into per-domain servers or consolidated further.
For a read-only data/diligence marketplace, the surface is quite comprehensive: weather, vehicle, crypto, SEC/finance, domain/email, sanctions, and geo workflows all have core operations plus fused verdict bundles. Minor gaps exist—such as a simple crypto price lookup or vehicle market value—but agents can usually work around them.