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search_issues_full

Search Redmine issues by keyword across subjects, descriptions, and all comments. Filter results by project or limit output for targeted issue discovery.

Instructions

Full-text search for issues by keyword. Returns results with description and all comments. Searches across subject, description, and all comments.

Args:
    query: Search keyword
    project_id: Filter by project ID (all projects if omitted)
    limit: Maximum number of results (all results if omitted)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYes
project_idNo
limitNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Implementation Reference

  • The MCP tool registration and handler wrapper for search_issues_full.
    def search_issues_full(
        query: str,
        project_id: Optional[str] = None,
        limit: Optional[int] = None,
    ) -> List[Dict[str, Any]]:
        """Full-text search for issues by keyword. Returns results with description and all comments.
        Searches across subject, description, and all comments.
    
        Args:
            query: Search keyword
            project_id: Filter by project ID (all projects if omitted)
            limit: Maximum number of results (all results if omitted)
        """
        logger.info(f"tool=search_issues_full query={query!r} project_id={project_id}")
        try:
            return _client().search_issues_full(
                query=query,
                project_id=project_id,
                limit=limit,
            )
        except RedmineError as e:
            logger.error(f"search_issues_full error: {e}")
            raise
  • The implementation of search_issues_full, which performs a full-text search against the Redmine API and then fetches detailed issue data.
    def search_issues_full(
        self,
        query: str,
        project_id: Optional[str] = None,
        limit: Optional[int] = None,
    ) -> List[Dict[str, Any]]:
        try:
            params: Dict[str, Any] = {
                "q": query,
                "issues": 1,
                "titles_only": 0,
            }
            if project_id is not None:
                params["scope"] = "projects"
                params["project_id"] = project_id
            else:
                params["scope"] = "all"
    
            issue_ids: List[int] = []
            offset = 0
            page_size = 25
            while True:
                params["offset"] = offset
                params["limit"] = page_size
                resp = requests.get(
                    f"{self._url}/search.json",
                    params=params,
                    headers={"X-Redmine-API-Key": self._api_key},
                    timeout=30,
                )
                if resp.status_code != 200:
                    raise RedmineError(
                        f"search_issues_full failed: HTTP {resp.status_code} {resp.text}"
                    )
                data = resp.json()
                results = data.get("results", [])
                for r in results:
                    if r.get("type") == "issue":
                        issue_ids.append(r["id"])
                if limit is not None and len(issue_ids) >= limit:
                    issue_ids = issue_ids[:limit]
                    break
                total = data.get("total_count", 0)
                offset += page_size
                if offset >= total or not results:
                    break
    
            output = []
            for issue_id in issue_ids:
                full = self._redmine.issue.get(
                    issue_id, include=["journals"]
                )
                journals = _safe(full, "journals", [])
                output.append({
                    "id": full.id,
                    "subject": full.subject,
                    "description": _safe(full, "description", ""),
                    "status": _safe(_safe(full, "status"), "name", ""),
                    "tracker": _safe(_safe(full, "tracker"), "name", ""),
                    "priority": _safe(_safe(full, "priority"), "name", ""),
                    "assigned_to": _safe(_safe(full, "assigned_to"), "name", ""),
                    "updated_on": str(_safe(full, "updated_on", "")),
                    "journals": [
                        {
                            "notes": _safe(j, "notes", ""),
                            "created_on": str(_safe(j, "created_on", "")),
                            "user": _safe(_safe(j, "user"), "name", ""),
                        }
                        for j in journals
                        if _safe(j, "notes")
                    ],
                })
            return output
        except (AuthError, ForbiddenError) as e:
            raise RedmineError(f"Authentication failed: {e}") from e
        except Exception as e:
            raise RedmineError(f"search_issues_full failed: {e}") from e
Behavior3/5

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

No annotations are provided, so the description carries the full burden. It discloses that this is a read operation (search) and specifies the scope of search (subject, description, comments) and return format (description and all comments). However, it lacks details on permissions, rate limits, pagination, or error handling, which are important for a search tool.

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 front-loaded with the core purpose, followed by parameter details in a structured format. Every sentence adds value: the first defines the tool, the second clarifies search scope, and the parameter explanations are necessary for understanding defaults. No wasted words.

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

Completeness4/5

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

Given the tool's moderate complexity (3 parameters, no annotations, but with an output schema), the description is mostly complete. It covers purpose, usage, and parameters well. Since an output schema exists, it doesn't need to explain return values in detail, but could benefit from more behavioral context like error cases or performance notes.

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

Parameters4/5

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

Schema description coverage is 0%, so the description must compensate. It adds meaningful semantics for all three parameters: 'query' as the search keyword, 'project_id' for filtering by project (with default behavior), and 'limit' for maximum results (with default behavior). This goes beyond the schema's basic titles, providing practical usage context.

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

Purpose5/5

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

The description clearly states the specific action ('Full-text search for issues by keyword') and resource ('issues'), distinguishing it from siblings like 'list_issues' by specifying it searches across subject, description, and all comments. It explicitly mentions what it returns ('Returns results with description and all comments'), making the purpose unambiguous.

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?

The description provides clear context for when to use this tool ('Full-text search for issues by keyword') and implies alternatives by specifying what it searches across, but does not explicitly name when-not-to-use cases or direct alternatives like 'list_issues'. It gives practical guidance on optional parameters, aiding usage decisions.

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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