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juspay

FDEP MCP Server

by juspay

get_function_callees

Identify all functions called by a specific Haskell function to analyze dependencies and understand code flow in enterprise-scale codebases.

Instructions

Get all functions called by a specific function

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
function_nameYesName of the function
module_nameNoModule containing the function (optional)
limitNoMaximum number of callees to return
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries full burden for behavioral disclosure. It states what the tool does but lacks critical details: it doesn't specify if this is a read-only operation, what the output format looks like (e.g., list of function names, structured data), whether there are rate limits, or if it requires specific permissions. For a tool with no annotation coverage, this is a significant gap in transparency.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, efficient sentence that front-loads the core purpose without unnecessary words. Every part earns its place by clearly stating the action and target, making it easy to parse quickly.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the complexity of code analysis tools and the lack of annotations and output schema, the description is incomplete. It doesn't explain return values, error conditions, or behavioral traits like pagination or performance limits. For a tool with 3 parameters and many siblings, more context is needed to guide effective use.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, with clear parameter descriptions in the schema (e.g., 'function_name' as the name of the function, 'limit' with a default). The description adds no additional semantic context beyond implying the tool uses 'function_name' as input, so it meets the baseline of 3 where the schema does the heavy lifting without extra value from the description.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose with a specific verb ('Get') and resource ('functions called by a specific function'), making it immediately understandable. However, it doesn't explicitly differentiate from similar sibling tools like 'get_function_callers' (which gets callers rather than callees) or 'get_function_call_graph' (which might provide broader relationship data), missing full sibling differentiation.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives. With many sibling tools for code analysis (e.g., 'get_function_callers', 'enhanced_function_call_graph', 'find_cross_module_calls'), there's no indication of context, prerequisites, or comparisons, leaving the agent to guess based on tool names alone.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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