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Survey a topic across the library

shamela_recon
Read-onlyIdempotent

استكشاف: حجم الموضوع في المكتبة وأين يقع

Round-zero reconnaissance: how much does this library actually hold on a topic, and on which shelves, BEFORE a retrieval plan is committed to. Same measurement as shamela_root_stats but mode defaults to 'literal' — counting pages that carry the WORD as written rather than every AlKhalil root reading of it. That difference is the point: «الاستصناع» reports 393,498 pages across 7,405 books morphologically and 1,510 literally, and the first answer says every shelf holds the topic, which is not a plan. Returns the EXACT page total plus the distribution by category, Hijri century, book and author, in 6-9 ms. Read the distribution as corpus size PLUS topic, never as authority: التفسير tops nearly every profile because tafsir is simply the largest body of text here, not because it is where the question is answered. Use the by_category ranking to choose scope.category_ids for the searches that follow. Examples: shamela_recon({root:'الاستصناع'}), shamela_recon({root:'التيمم', scope:{citable_only:true}}).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modeNo'literal' counts pages carrying the WORD as written (plus the ordinary spelling folds). 'morphological' expands it to every AlKhalil root reading and counts those — which is far broader than most callers expect: «الاستصناع» reports 393,498 pages across 7,405 books morphologically against 1,510 literally, an answer that says every shelf holds the topic. Use 'literal' to size a topic before planning retrieval; use 'morphological' to trace a ROOT through its derivations.literal
rootYesArabic root or base word to profile (e.g. «صبر»، «رحم»، «علم»). Under mode='morphological' it is matched via the AlKhalil analyzer, so all derived forms (صابر، يصبر، اصطبار…) are counted, not just the literal string.
scopeNoRestrict the profile to specific books, authors, categories, or a Hijri date range. Use shamela_list_categories / shamela_resolve to find IDs. Omit to profile the whole downloaded library.
response_formatNoOutput format for the human-readable text. 'markdown' (default) is best for direct display; 'json' returns the same data as a single JSON code block; 'none' skips the rendered text entirely and returns a one-line pointer, leaving `structuredContent` as the whole answer. The structured data is returned in `structuredContent` regardless of this setting, so a caller that parses it should pass 'none' — the character budget is measured over BOTH channels, and the unread copy is otherwise spending most of it. Requires the 'response_format.none' capability (see shamela_brief).markdown

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.8/5.0
Behavior5/5

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

Although annotations already declare the safety profile, the description adds substantial behavioral context: exact return composition, runtime (6–9 ms), the literal-vs-morphological counting difference with a concrete worked example, and a crucial interpretation caveat about tafsir inflating profiles. That goes well beyond what the annotations provide.

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 purpose is front-loaded and most sentences carry weight, including the comparison, return description, warning, and examples. It is somewhat long for a description and could be trimmed, but the complexity of the tool and nested scope justify the additional length.

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

Completeness5/5

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

There is no output schema, so the description must carry the return-value burden, and it does: it describes the exact page total, distribution dimensions, and speed. Combined with the 100%-covered input schema and annotations covering safety, the description is complete enough for correct invocation.

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 coverage is 100%, so the schema already documents all parameters in depth. The description adds operational meaning by explaining that the by_category output should drive scope.category_ids in later searches and by showing scope usage in the examples, but it does not add much syntax beyond the schema.

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 names a specific operation (round-zero reconnaissance) and a clear resource (topic coverage across the library), and explicitly distinguishes it from shamela_root_stats by noting the different default mode. An agent can tell what the tool produces and how it differs from the nearest sibling without opening either schema.

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

Usage Guidelines5/5

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

It states the precise moment to use the tool: before committing to a retrieval plan. It also gives mode-selection guidance, warns against reading distribution as authority, and directs the caller to use the by_category ranking to set scope.category_ids for follow-up searches, which is explicit when-to-use and how-to-continue guidance.

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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