AL-Go MCP Server

Semantic Search

alg-semantic-search

Find relevant AL-Go content across workshops, scenarios, discussions, and issues using cross-source relevance scoring to answer development automation questions.

Instructions

Intelligent cross-source search across workshops, scenarios, discussions, and issues using relevance scoring

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "query": "<query>"
    },
    "name": "alg-semantic-search"
  }
}

Input Schema

Name	Required	Description	Default
`limit`	No	Maximum results to return
`query`	Yes	Search query

Schema Changelog

Changes observed during successful MCP inspections.

Changed3 schema fields changedv2.9.0Oct 19, 2026
- addedInput schema / properties / limit / maximum
```
Added value: +50
```
- addedInput schema / properties / limit / minimum
```
Added value: +1
```
- changedInput schema / properties / limit / type
```
Previous value: -"number"New value: +"integer"
```
First observedv2.8.0Jul 29, 2026

TDQS

B3.2/5.0

Behavior2/5

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

No annotations are provided, so the description carries the full behavioral burden. 'Relevance scoring' is a mild disclosure about ranking, but it says nothing about latency, index/cache dependency (relevant given alg-refresh-cache and alg-cache-stats siblings), result grouping, or whether results carry source labels.

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?

One front-loaded sentence that identifies the action, scope, and ranking mechanism with no wasted clauses. 'Intelligent' is mild marketing filler, which keeps it from a 5.

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

Completeness3/5

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

The tool is low-complexity and the input schema is fully self-documented, so the description is close to sufficient. With no output schema, however, it should say something about what comes back (e.g. mixed-source results, relevance ordering, source attribution), and it does not.

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 only two flat parameters, so the schema already documents 'query' and 'limit' including default 10 and max 50. The description adds no syntax, matching, or filtering guidance beyond that, so the baseline 3 is correct.

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?

Names a specific verb (search) and the resource set (workshops, scenarios, discussions, issues), and 'cross-source' distinguishes it from the single-source siblings alg-search-docs, alg-search-discussions, and alg-search-issues. It stops short of explicitly naming a sibling it is not, so it falls just under the top band.

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

Usage Guidelines3/5

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

'Cross-source' implies the tool is for broad searches rather than the per-source siblings, which is useful implicit routing. However, there is no explicit when-to-use statement, no exclusion (e.g. 'do not use for a single known source'), and no mention of the cache/refresh siblings.

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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