Loading ToolBench
An MCP server for evaluating credit approvability for customers using PayPal's credit risk assessment APIs
Single tool with mixed quality. The tool is explicitly registered via @mcp.tool() decorator with a complete implementation and schema visible in source code. However, the naming, descriptions, and parameter handling have significant gaps that would confuse an LLM agent in production. The tool name 'evaluate_credit_approvability' is domain-specific but overly long (32 chars, above p90=27). Parameter descriptions are present but terse (avg ~40 chars vs baseline 72). Most critically, the tool accepts multiple parameters with hardcoded defaults that should not be required parameters at all, and the description lacks context on when to use this tool vs alternatives or what happens when called with mock vs real API. The schema is properly typed JSON Schema with all parameters defined, which is the primary strength. Error handling exists but returns free-text strings rather than structured responses suitable for LLM parsing.
Evaluate credit approvability for a customer.
Get weather alerts for a US state.
Get weather forecast for a location.
Tool name is 32 characters (above p90=27 baseline) and uses passive voice 'evaluate_credit_approvability' rather than active verb_noun pattern. 'approve_credit' or 'check_credit_eligibility' would be more concise and LLM-friendly.
Tool description is only 42 characters: 'Evaluate credit approvability for a customer.' This is below the 10 - 1024 character guideline and provides zero context on WHEN to use this tool, what the output means, or how mock mode differs from real API mode. LLM cannot determine when to select this tool or how to interpret results.
Nine parameters with hardcoded defaults (country_code='1', phone_number='3046738654', user_id='110171134', product_id='CREDIT_CARD_US', use_mock=True) should not all be required positional parameters. This forces the LLM to reason about which defaults to override. Only 'email', 'full_name', 'given_name', 'surname' are truly required; others should have smart defaults or be optional.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-22 | F | 5 | 2026-07-28+ | v2 |
| 2026-03-09 | F | 42 | - | v1 |
Parameter descriptions are terse (15 - 30 chars each). E.g., 'Customer's email address' lacks context on format (must be valid email). 'Phone country code (default: "1" for US)' is under 50 chars but does not state that only numeric values are accepted or valid range. Descriptions below ~50 chars invite LLM errors.
No enum constraint on 'product_id' parameter. Valid values (CREDIT_CARD_US, etc.) are hardcoded as default but not declared as an enum. LLM could hallucinate invalid product types. Should declare: enum=['CREDIT_CARD_US', 'CREDIT_CARD_CA', ...]
Output is unstructured free-text string. Response is a human-readable multi-line string ('Credit Approvability Decision: APPROVED\nReason: ...') rather than structured JSON. LLM must parse text to extract decision, reason, and eligibility flag. Structured output would enable reliable downstream tool chaining.
Mock mode vs real API mode is not clearly distinguished in description. The 'use_mock' parameter defaults to True, which means by default the tool ignores the real API entirely. Description should explicitly state 'Returns mock data by default; set use_mock=false to call real PayPal API (requires valid credentials).'
Error handling returns free-text error strings (e.g., 'Error evaluating credit approvability: {message}') rather than structured error responses with recovery guidance. Rubric requires: 'Error responses must tell the LLM what to do next.' A timeout error should suggest retry; a credential error should suggest checking API keys.
Default value for 'phone_number' is a real-looking number '3046738654', and default 'user_id' is '110171134'.
No output schema documentation. Return type is annotated as '-> str' in the function signature, but no structured schema is provided to describe what fields the LLM should expect or how to parse the response. Baseline: '100% of A+ tools have documented return types.'