Get bounded GSE loan-performance analytics for one clearly defined topic.
| Name | Required | Description | Default |
|---|---|---|---|
| gse | No | all | |
| top_n | No | ||
| topic | Yes | ||
| to_date | No | ||
| from_date | No | ||
| max_points | No |
| Name | Required | Description | Default |
|---|---|---|---|
| data | No | ||
| meta | Yes |
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 idempotentHint=true, so the safety profile is clear. The description adds 'bounded', hinting at output limits or scoping, but does not detail rate limits, pagination, or other behavioral traits beyond what annotations provide.
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 single-sentence description is concise but overly brief. It front-loads the core purpose but sacrifices important details, making it efficient yet insufficient for a tool with six parameters.
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 tool's complexity (6 parameters, enums, date ranges), the description is incomplete. It does not explain 'bounded', how topics map to analytics, date usage, or the meaning of top_n and max_points. The presence of an output schema mitigates return value documentation, but parameter usage remains underdescribed.
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 0%, and the description does not explain any parameter beyond implying topic selection. Critical parameters like gse, top_n, dates, and max_points have no semantic guidance, forcing the agent to rely on enum names and defaults.
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 purpose: retrieving 'GSE loan-performance analytics' with qualifiers 'bounded' and 'one clearly defined topic', which distinguishes it from broader sibling tools like get_market_snapshot or get_servicer_analytics, though it does not explicitly name 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?
No explicit guidance on when to use this tool versus siblings. The description does not mention conditions for use, prerequisites, or exclusions, leaving the agent to infer from the tool name and 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.
Most tools target clearly distinct data domains: lender search/profile/compare cluster around HMDA lender analytics, while market snapshot, loan performance, servicer analytics, and SEC filing tools are separate. The only minor ambiguity is between get_lender_profile, compare_lenders, and search_lenders which all deal with lender keys, though their intents (search vs. profile vs. comparison) are distinct.
Tools follow a consistent get_/search_/compare_ verb pattern with clear noun targets (lenders, market_snapshot, loan_performance, sec_filing). The one deviation is that search_lenders, search_sec_filings, and compare_lenders use non-get verbs while the rest use get_, but this is a sensible pattern matching read vs. search semantics.
At 9 tools, this is well within the ideal 3-15 range and each tool appears to represent a meaningful consumer capability for a mortgage analytics domain. The count feels appropriately scoped without redundancy or bloat.
The surface covers market data, SEC filings, lender profiles, loan performance, and servicer analytics, but there are notable gaps: there's no mutation capability (all read-oriented), no way to combine or join across these domains, and no search tool for loan performance or market data topics - those are limited to pre-bounded topics. Some domains feel like they have single query points rather than full browsing coverage.
