FIRST EPSS exploit prediction score for a CVE. Returns probability (0-1) of exploitation in the next 30 days plus the percentile rank.
| Name | Required | Description | Default |
|---|---|---|---|
| cve_id | Yes | CVE identifier. |
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, idempotentHint=true, and destructiveHint=false, so the safety profile is covered. The description adds meaningful behavioral context beyond annotations by specifying the output semantics: a 0-1 probability, a 30-day lookahead window, and a percentile rank. It could add edge-case behavior (e.g., behavior for unknown/malformed CVE IDs), but the core behavior is transparent.
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?
Two sentences with zero filler: the first identifies the tool's unique function, the second enumerates exactly what is returned. The key scoping detail (EPSS prediction for a CVE) is front-loaded, and every phrase earns its place.
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 single-parameter, read-only lookup tool with openWorldHint and no output schema, the description covers the essential contract: input (CVE) and output (probability plus percentile). The main gap is error and edge-case behavior (invalid or nonexistent CVE IDs, data availability for recent CVEs), but given the tool's simplicity and the annotations, the description is reasonably complete.
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 coverage is 100% for the single parameter cve_id, whose schema description is 'CVE identifier.' The description echoes the concept of a CVE without adding format details (e.g., CVE-YYYY-NNNNN pattern) or input constraints. The schema carries the parameter documentation, so baseline 3 with no additional description value 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 states a specific resource (a CVE) and a specific, well-known score (FIRST EPSS exploit prediction score), then clarifies the exact return semantics: probability (0-1) of exploitation in the next 30 days plus percentile rank. This clearly distinguishes it from sibling tools like cve_lookup or kev_status_check, which serve different purposes.
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 description implies usage—you call this when you need an EPSS score—but it never explicitly states when to prefer this over related siblings such as kev_status_check, cve_lookup, or cve_search_by_keyword. No exclusions or alternative routing is provided, so the agent must infer the distinction from context alone.
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.
Several tool clusters overlap heavily—company due-diligence and risk tools (counterparty_risk_score, company_trust_check, entity_dossier, issuer_diligence_dossier, resolve_entity, entity_resolve), carrier vetting tools, sanctions screening tools, and recall tools all have subtle boundary distinctions. While descriptions are detailed, an agent navigating 294 tools will frequently struggle to pick the right one.
Most tools follow a readable snake_case domain-prefix pattern (fdic_, edgar_, sanctions_, congress_), which helps. However, verb placement is inconsistent—search_available_datasets vs cdc_dataset_query, resolve_entity vs entity_resolve—and synonyms like search, lookup, get, detail, fetch, and status are used interchangeably.
294 tools is an extreme number for a single MCP server, far beyond what an agent can reliably hold in context or select from accurately. The presence of tool-group discovery helpers mitigates but does not solve the fundamental scale problem.
The data breadth is genuinely extensive, covering finance, health, legal, real estate, transportation, energy, cyber, education, and many other domains, often with generic query fallbacks. Still, some capabilities are shallow or incomplete—package tracking stops at a link, property tools are demo-only in places, and caselaw coverage is limited—so it is not a fully complete surface.