get_jsfingerprint_similarity_countsGet JS fingerprint similarity counts.
| Name | Required | Description | Default |
|---|---|---|---|
| max_count | No | ||
| threshold | No | ||
| fingerprint_id | Yes |
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, but the description adds no behavioral information beyond the title. It does not explain the effect of threshold or max_count, nor does it describe the shape of the returned counts.
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 concise but under-specified; it is a single tautological sentence that adds no information beyond the title. It is not a case of efficient communication but rather missing content.
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 large set of sibling tools and the terse description, the tool is not adequately defined. The output schema and annotations provide some structure, but the description fails to clarify the tool's unique role, parameter semantics, or selection criteria, making it nearly impossible for an agent to use 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?
With schema description coverage at 0%, the description was expected to compensate by explaining the parameters, but it does not mention fingerprint_id, threshold, or max_count at all. The agent cannot infer what these parameters mean or how they affect the output.
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 restates the tool's name/title without explaining what a 'similarity count' is or how it relates to the fingerprint. It does identify a specific verb and resource, but the resource term is opaque, so it fails to convey the tool's actual function in a way that distinguishes it from sibling tools like get_jsfingerprint_similar.
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?
No guidance is provided on when to use this tool relative to its many siblings. The description does not mention alternatives, prerequisites, or any selection criteria, leaving the agent to guess.
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 overlap in purpose, particularly the JS fingerprint search functions (e.g., search_js_fingerprint_by_md5 and search_jsfingerprints_by_md5) which are nearly identical. The large number of get_* and search_* tools for various statistics also creates boundary ambiguity.
The verb_noun pattern is mostly followed, but there are significant inconsistencies: 'jsfingerprints' vs 'js_fingerprints' vs 'js_fingerprint', and the occasional use of 'find' instead of 'search' (e.g., find_js_fingerprint_similar_by_hash). This mixed style makes it hard to predict tool names.
With 128 tools, the server is severely over-scoped. Many tools could be combined (e.g., all search_jsfingerprints_by_* variants) or parameterized. The sheer number overwhelms an agent and suggests poor API design.
The core URL scanning workflow (submit, retrieve results, search, analyze) is well covered, including detailed sub-analyses like malware, YARA, and JS fingerprints. Minor gaps include no scan cancellation or user-specific scan listing, but overall coverage is strong.