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Tools for Agents

by io.github.max124vip-dev

csv_parse

Parse CSV/TSV into rows and records

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
textYesRaw CSV/TSV text
delimiterNoField delimiter,
has_headerNo

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Changed5 schema fields changed
    • removedInput schema / properties / args
      Removed value: -{
-  "description": "Tool arguments",
-  "properties": {
-    "text": {
-      "description": "Primary input text",
-      "type": "string"
-    }
-  },
-  "type": "object"
-}
    • addedInput schema / properties / delimiter
      Added value: +{
+  "default": ",",
+  "description": "Field delimiter",
+  "type": "string"
+}
    • addedInput schema / properties / has_header
      Added value: +{
+  "default": true,
+  "type": "boolean"
+}
    • addedInput schema / properties / text
      Added value: +{
+  "description": "Raw CSV/TSV text",
+  "type": "string"
+}
    • changedInput schema / required
      Previous value: -[]New value: +[
+  "text"
+]
  2. Added

TDQS

B3.3/5.0
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 burden of behavioral disclosure. It only states the high-level action and does not mention delimiter handling, header behavior, quoting, output structure, or error handling, leaving the agent with significant unknowns.

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 a single, front-loaded sentence with zero wasted words. It efficiently communicates the core purpose without redundancy.

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?

Given no output schema and no annotations, the description is too sparse. It doesn't specify the return format (e.g., array of arrays vs. array of objects), how 'has_header' influences output, or any edge cases like empty lines or custom delimiters. The tool needs more context to be invoked reliably.

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?

The schema describes 'text' and 'delimiter' but not 'has_header'. The description adds no parameter-specific meaning—it doesn't clarify how 'has_header' affects parsing or what 'records' means. With moderation schema coverage (67%), the description fails to compensate for the missing parameter info.

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 uses a specific verb 'Parse' with a clear resource 'CSV/TSV' and outcome 'rows and records'. It distinguishes this tool from other parsing siblings like json_parse or xml_parse by naming the exact format.

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?

The description implies usage for parsing CSV/TSV text, but offers no explicit guidance on when to use it versus alternatives, nor any exclusions or preconditions. It's a basic implied usage scenario.

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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TDQS

C2/5.0
Disambiguation1/5

Multiple tools have overlapping or identical purposes, such as ocr_url and ocr_image (both OCR from an image URL), compare_texts and text_diff (both compare or diff texts), extract_url and read_url (both extract webpage content), and content_hash and hash_text (both compute hashes). The boundaries between these tools are unclear, causing a high risk of misselection.

Naming Consistency2/5

Naming conventions are mixed. Many tools use verb_noun (extract_url, validate_email), but others use noun_verb (language_detect, html_clean), single words (advisor, crawl, retrieve), or noun_noun (job_status, page_metadata). This inconsistency makes it harder to predict tool names.

Tool Count1/5

With 100 tools, the server is extremely over-scoped for a generic agent toolkit. While some tools are distinct and useful, the sheer number does not align with a focused purpose; many tools are redundant or highly specialized, and the count exceeds what is typically manageable for an agent to reason about.

Completeness3/5

The toolkit covers a broad range of utilities including extraction, validation, processing, research, memory, and orchestration. However, there are no CRUD tools for creating/updating/deleting resources, no database or file system operations, and no integration beyond web/API basics. This leaves significant gaps for agents that need general lifecycle management, though it does handle many common tasks.

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