Summarize a company's federal awards: total dollars and top awards for a recipient name in a category (contracts by default). Useful for due diligence and to see who the government pays.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Top awards to list (default 5, max 25). | |
| category | No | Award category: 'contracts' (default), 'grants', 'loans', or 'other'. | |
| recipient | Yes | Recipient company/org name. |
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 establish that the tool is read-only, idempotent, and non-destructive, so the description's safety burden is lower. The description adds useful behavioral detail by specifying the output shape (total dollars and top awards), the category default, and the recipient-based aggregation, but it does not disclose data sources, time periods, or potential result limitations.
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 two sentences with no fluff. The first sentence front-loads the core purpose and output, while the second adds a brief use-case rationale. Every clause contributes to agent understanding.
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 three-parameter tool with no output schema, the description gives a reasonable sense of what will be returned (total dollars and top awards) and the default category. However, it leaves ambiguity about the time period, whether totals are across all federal spending or a specific year, and what fields the 'top awards' include, which an agent might need to set expectations.
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 all three parameters are already documented with meaningful descriptions. The tool description adds little beyond reinforcing the recipient-name focus and the contracts default, which the schema already covers. A baseline score of 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 states a specific verb ('summarize') and resource ('a company's federal awards'), and clearly identifies the output: total dollars and top awards by recipient within a category. It implicitly distinguishes itself from sibling tools like spending_award_details and spending_search_awards by framing this as a summary view, though it does not name those alternatives.
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 offers use-case context ('useful for due diligence and to see who the government pays') and notes the contracts default, which gives some guidance on when to invoke it. However, it does not explicitly compare against sibling tools such as spending_award_details or spending_search_awards, nor state when this tool should be preferred over them.
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.