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furl_retrieve

Retrieve original content by hash from compressed entries. Search across all stored entries or filter by pattern, fields, or row selection to extract specific data.

Instructions

Retrieve original uncompressed content by hash. Use this when you need full details from previously compressed content. The hash comes from furl_compress results or from compression markers like [N items compressed... hash=abc123]. Two extra modes: (1) OMIT hash and pass query to search across ALL stored entries (returns ranked hash/score/preview matches to retrieve individually); (2) pass hash with pattern/fields/line_range, or a select_field row-filter (keep the rows of a JSON array by exact value or numeric range), to project just part of the original. Filters cannot be combined with query. Examples: furl_retrieve(hash) -> the whole original; furl_retrieve(hash, pattern="ERROR") -> only the matching lines; furl_retrieve(hash, select_field="id", select_equals=42) -> the rows where id==42.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
hashNoHash key from compression (e.g., 'abc123' from hash=abc123). Omit to search across all entries via 'query'.
limitNoMax rows a select_field row-select returns (positive integer; defaults to 1000 when a select is requested without it). When more rows match, only the first 'limit' ship plus one explicit truncation-marker row. Applies only to select_field row-selects.
queryNoSearch query. WITH a hash: return only items in that entry matching the query. WITHOUT a hash: full-text search (BM25-ranked) across every stored entry, returning top matches as hash/score/preview. Mutually exclusive with pattern/fields/line_range.
fieldsNoFor a JSON-array original: project only these keys out of each object element (requires a hash, no query). Errors if the original is not a JSON array. Cannot be combined with pattern/line_range; composes with select_field (projects the columns of the kept rows).
patternNoRegex applied line-by-line to the full original (requires a hash, no query). Returns matching lines (prefixed with 1-based line numbers) plus 'context_lines' lines of surrounding context. Invalid regex returns an error.
line_rangeNo[start, end] 1-based inclusive line window over the full original (requires a hash, no query). Either bound may be null for an open end. Composes with 'pattern' (the range is applied first).
select_maxNoNumeric-range mode: keep rows whose select_field is a number <= select_max (inclusive; open upper bound when omitted). Must be >= select_min. Mutually exclusive with select_equals.
select_minNoNumeric-range mode: keep rows whose select_field is a number >= select_min (inclusive; open lower bound when omitted). A row whose field is missing or non-numeric is skipped, never an error. Mutually exclusive with select_equals.
select_fieldNoRow-select over a JSON array of objects (requires a hash, no query): the field/column name to match on. It anchors the whole select family — select_equals / select_min / select_max / limit are honored ONLY alongside select_field (any of them without it is an error). Reads a top-level JSON array of objects OR a JSON object with exactly one dominant inner array (e.g. a '{metadata, traceEvents:[...]}' trace). Composes with 'fields'; cannot be combined with pattern/line_range or query.
context_linesNoLines of context to include on each side of a 'pattern' match (default 0, max 50).
select_equalsNoEquality mode: keep rows whose select_field equals this JSON scalar (string/number/boolean/null; a list or object is rejected). Bool-safe — true never matches the number 1. Mutually exclusive with select_min/select_max.
Behavior5/5

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

No annotations provided, but description thoroughly covers behavioral traits: retrieval, search, filtering, parameter constraints, default values, error conditions (invalid regex, type mismatches), truncation behavior for limit. No contradictions.

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?

Description is long but well-structured with paragraphs and examples. Front-loaded main purpose. Could be more concise (e.g., example lines could be shortened), but clarity is not sacrificed.

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?

For a tool with 11 parameters (0 required) and no output schema, description covers all key aspects: usage modes, parameter interactions, defaults, error handling, and return format hints. Complete for an AI agent to invoke correctly.

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

Parameters5/5

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

Schema coverage is 100%, but description adds significant meaning: explains two modes, mutual exclusivity, default for limit, select_field behavior with JSON arrays, composes with fields. Goes well beyond schema definitions.

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 tool retrieves original uncompressed content by hash, and explains two additional modes. It distinguishes from sibling tools like furl_compress and furl_search by specifying the source of hash and search capabilities.

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?

Describes when to use for retrieving full details, searching across entries, or projecting parts. Includes examples and mutual exclusivity rules. Could explicitly mention when not to use (e.g., for listing all entries, use furl_list instead), but overall adequate.

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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