Skip to main content
Glama
SK-DEV-AI

codesearch MCP Server

by SK-DEV-AI

so_search

Search Stack Overflow via Stack Exchange API for accepted answers and tag-filtered Q&A, or query SOFA for agent-contributed content with trust scores.

Instructions

Stack Overflow: Stack Exchange API (free, 300 req/day, resolved/accepted answers with score/tags) or SOFA (Stack Overflow for Agents, beta, agent-contributed content with trust scores, requires SOFA_KEY env). Use stackexchange for authoritative resolved answers, sofa for fresher agent-contributed content. e.g. so_search(query='python async', tags='python', accepted=true)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
idsNoComma-separated IDs for questions_by_ids / answers_by_ids / users_by_ids
pageNoPage number for pagination
siteNostackoverflow
sortNorelevance
tagsNo
typeNosearch
countNo
queryNo
viewsNoMinimum view count
actionNostackexchange=official API (free, 300 req/day, resolved answers, score/tags/views/activity). sofa=Stack Overflow for Agents (beta, agent-contributed, trust scores, needs SOFA_KEY). questions_by_ids/search_users/search_tags/question_comments/questions/answers/users are Stack Exchange sub-actionsstackexchange
closedNo
todateNoUnix timestamp or date string for latest creation date
answersNoMinimum answer count
post_idNoSOFA only: get post by ID instead of searching
acceptedNo
fromdateNoUnix timestamp or date string for earliest creation date
steeringNoSOFA only: curation steering parameter
question_idNo
content_typeNoSOFA only: question|til|blueprint|playbookquestion

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.1

TDQS

B3.3/5.0
Behavior3/5

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

With no annotations, the description carries the full behavioral burden. It usefully discloses the Stack Exchange rate limit (300 req/day), SOFA beta status, and SOFA_KEY environment requirement, but it does not describe pagination behavior, output shape, or whether all sub-actions are read-only.

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

Conciseness4/5

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

The description is compact and front-loads the key distinction between the two backends. The example is useful rather than filler, though the first sentence is dense with parenthetical detail.

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?

For a 19-parameter tool with no output schema and no annotations, the description is not complete enough. It covers the main backend choice and some operational constraints, but it omits how the many sub-actions interact with parameters, pagination, and return values.

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

Parameters2/5

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

Schema description coverage is only 53% for 19 parameters, and the description compensates only partially by explaining the action backends and giving an example using query, tags, and accepted. Many parameters such as site, sort, type, count, closed, answers, question_id, and content_type remain undocumented in 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 identifies Stack Overflow as the resource and distinguishes the two backends, Stack Exchange and SOFA, with specific attributes like resolved answers and trust scores. It is clear what the tool does, though it does not explicitly differentiate from sibling tools such as search_all or code_search.

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

Usage Guidelines4/5

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

It explicitly states when to choose stackexchange versus sofa: authoritative resolved answers versus fresher agent-contributed content. It gives an example call, but does not explain when to use this tool instead of sibling tools like search_all or code_search.

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