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BigQuery Validator

Name: BigQuery Validator
Author: caron14

by caron14

Overview Schema Source Related Servers Discussions Admin

Python

Hybrid

mcp-bigquery

Sichere BigQuery-Erkundung über das Model Context Protocol

MIT License PyPI Version Python Support Downloads

Dokumentation | Schnellstart | Beispiele

Überblick

mcp-bigquery ist ein Server für das Model Context Protocol (MCP), der es KI-Assistenten (wie Claude) ermöglicht, sicher mit Google BigQuery zu interagieren.

Hauptfunktionen

Sichere Ausführung: Alle Vorgänge sind strikt auf Dry-Run-Überprüfungen beschränkt. Der Server führt niemals Abfragen aus, die Daten verändern oder Ausführungskosten verursachen.
Kostentransparenz: Liefert Schätzungen zu Abfragekosten und verarbeiteten Bytes vor der Ausführung.
Statische Analyse: Analysiert Abfrageabhängigkeiten und validiert die SQL-Syntax.
Schemaerkundung: Durchsucht Datasets, Tabellen und Spalten.

Geschäftlicher Nutzen

Problem	Lösung mit mcp-bigquery
Unbeabsichtigte Ausführung teurer Abfragen	Kostenschätzung vor der Ausführung
Verzögerte Entwicklung durch SQL-Syntaxfehler	Früherkennung von Syntaxfehlern
Fehlende Transparenz über Schemastrukturen	Sichere Ermittlung von Schemametadaten
Risiko unbefugter Datenänderungen durch KI	Erzwungene Dry-Run-Einschränkungen

Related MCP server: BigQuery MCP Server

Schnellstart

Schritt 1: Installation

Installieren Sie das Paket über pip:

pip install mcp-bigquery

Schritt 2: Authentifizierung

Richten Sie die Google-Cloud-Plattform-Authentifizierung ein:

# For user account authentication
gcloud auth application-default login

# For service account authentication
export GOOGLE_APPLICATION_CREDENTIALS=/path/to/key.json

Schritt 3: Claude-Desktop-Konfiguration

Konfigurieren Sie den Server in der Claude-Desktop-Konfigurationsdatei:

macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
Windows: %APPDATA%\Claude\claude_desktop_config.json

Fügen Sie den folgenden Eintrag hinzu:

{
  "mcpServers": {
    "mcp-bigquery": {
      "command": "mcp-bigquery",
      "env": {
        "BQ_PROJECT": "your-gcp-project-id"
      }
    }
  }
}

Schritt 4: Überprüfung

Starten Sie Claude Desktop neu und führen Sie die folgenden Abfragen aus, um die Einrichtung zu überprüfen:

„Welche Datasets sind in meinem BigQuery-Projekt verfügbar?“
„Können Sie die Kosten schätzen für: SELECT * FROM dataset.table“
„Zeigen Sie mir das Schema für die Tabelle users an“

Verfügbare Tools

SQL-Validierung und -Analyse

Tool	Zweck	Hauptanwendungsfall
bq_validate_sql	SQL-Syntax prüfen	Überprüfung vor der Abfrageausführung
bq_dry_run_sql	Kostenschätzungen und Metadaten abrufen	Kostenbewertung vor der Ausführung
bq_extract_dependencies	Tabellenabhängigkeiten abbilden	Linien- und Abhängigkeitszuordnung
bq_validate_query_syntax	Detaillierte Syntaxanalyse	Debuggen komplexer SQL-Abfragen

Schemaerkennung

Tool	Zweck	Hauptanwendungsfall
bq_list_datasets	Alle Datasets im Projekt auflisten	Erste Projekterkundung
bq_list_tables	Tabellen mit Partitionsmetadaten auflisten	Durchsuchen der Dataset-Struktur
bq_describe_table	Detaillierte Schemainformationen abrufen	Überprüfung auf Spaltenebene
bq_get_table_info	Umfassende Metadaten abrufen	Analyse der Tabellenstatistiken
bq_preview_table	Tabellendaten in der Vorschau anzeigen (kostenlos)	Beispielsätze ohne Daten-Scan-Kosten prüfen

[!IMPORTANT] Das Tool bq_preview_table verwendet client.list_rows (API: tabledata.list), um Beispielzeilen direkt abzurufen, was zu null gescannten Bytes und keinen Ausführungskosten führt. Um eine unbeabsichtigte Offenlegung vertraulicher Informationen (z. B. personenbezogener Daten) gegenüber dem LLM zu verhindern, ist dieses Tool standardmäßig deaktiviert. Sie müssen es explizit aktivieren, indem Sie MCP_BQ_ENABLE_PREVIEW=true in Ihrer Umgebungskonfiguration festlegen.

Konfiguration

Umgebungsvariablen

Variable	Zweck	Standard
`BQ_PROJECT`	Ziel-GCP-Projekt-ID	Wird über ADC ermittelt
`BQ_LOCATION`	Ziel-BigQuery-Region	Nicht festgelegt
`SAFE_PRICE_PER_TIB`	Preis pro TiB für die Kostenschätzung	5.0
`LOG_LEVEL`	Ausführlichkeit der Protokollierung (DEBUG, INFO, WARNING, ERROR, CRITICAL)	WARNING
`MCP_BQ_ENABLE_PREVIEW`	Aktiviert das Tool bq_preview_table (true/false)	false

Beispiel-.env-Datei

Für lokale Tests oder Entwicklungsumgebungen können Sie diese Variablen in einer .env-Datei definieren:

BQ_PROJECT=your-gcp-project-id
BQ_LOCATION=asia-northeast1
SAFE_PRICE_PER_TIB=5.0
LOG_LEVEL=WARNING
MCP_BQ_ENABLE_PREVIEW=true

Vollständiges Konfigurationsbeispiel für Claude Desktop

{
  "mcpServers": {
    "mcp-bigquery": {
      "command": "mcp-bigquery",
      "env": {
        "BQ_PROJECT": "my-production-project",
        "BQ_LOCATION": "asia-northeast1",
        "SAFE_PRICE_PER_TIB": "6.0",
        "LOG_LEVEL": "WARNING",
        "MCP_BQ_ENABLE_PREVIEW": "true"
      }
    }
  }
}

Fehlerbehebung

Zugeordnete Fehler und Lösungen

Authentifizierungsfehler

Error: Could not automatically determine credentials

Lösung: Authentifizieren Sie sich über die Befehlszeile neu:
```
gcloud auth application-default login
```

Zugriff verweigert

Error: User does not have bigquery.tables.get permission

Lösung: Weisen Sie der Zielidentität die Rolle BigQuery Data Viewer zu:

gcloud projects add-iam-policy-binding YOUR_PROJECT \
  --member="user:your-email@example.com" \
  --role="roles/bigquery.dataViewer"

Projekt-ID fehlt

Error: Project ID is required

Lösung: Stellen Sie sicher, dass die Variable BQ_PROJECT in Ihrer Konfiguration korrekt festgelegt ist.

Anwendungsbeispiele

Beispiel 1: Kosten vor der Ausführung prüfen

# Before running an expensive query...
query = "SELECT * FROM `bigquery-public-data.github_repos.commits`"

# First, check the cost
result = bq_dry_run_sql(sql=query)
print(f"Estimated cost: ${result['usdEstimate']}")
print(f"Data processed: {result['totalBytesProcessed'] / 1e9:.2f} GB")

# Output:
# Estimated cost: $12.50
# Data processed: 2500.00 GB

Beispiel 2: Tabellenstruktur verstehen

# Check table schema
result = bq_describe_table(
    dataset_id="your_dataset",
    table_id="users"
)

# Output:
# ├── user_id (INTEGER, REQUIRED)
# ├── email (STRING, NULLABLE)
# ├── created_at (TIMESTAMP, REQUIRED)
# └── profile (RECORD, REPEATED)
#     ├── name (STRING)
#     └── age (INTEGER)

Beispiel 3: Datenabhängigkeiten nachverfolgen

# Understand query dependencies
query = """
WITH user_stats AS (
  SELECT user_id, COUNT(*) as order_count
  FROM orders
  GROUP BY user_id
)
SELECT u.name, s.order_count
FROM users u
JOIN user_stats s ON u.id = s.user_id
"""

result = bq_extract_dependencies(sql=query)

# Output:
# Tables: ['orders', 'users']
# Columns: ['user_id', 'name', 'id']
# Dependency Graph:
#   orders → user_stats → final_result
#   users → final_result

Projektstatus und Versionsverlauf

Version	Veröffentlichungsdatum	Zusammenfassung der Änderungen
v0.7.1	2026-08-17	Verfeinerte mcp-Abhängigkeitsbeschränkungen und optimierte Wiki-Dokumentation
v0.7.0	2026-06-21	Kostenloses Tabellenvorschau-Tool (`bq_preview_table`) und Sicherheits-Opt-in-Konfiguration hinzugefügt
v0.6.0	2026-06-21	Thread-sicherer Cache, rekursive AST-Abfragen, Backoff-Wiederholungen und Google-API-Ausnahmebehandlung
v0.5.0	2026-01-02	Konsolidierte Formatierer, Client-Cache und einheitliche Protokollierungssteuerung
v0.4.2	2025-12-08	Modulares Schema-Explorer- und einheitliche Client-/Protokollierungssteuerung
v0.4.1	2025-01-22	Verbesserungen bei der Fehlerbehandlung und beim Debug-Logging
v0.4.0	2025-01-22	Tools zur Schemaerkennung hinzugefügt
v0.3.0	2025-01-17	Integrierte statische SQL-Analyse-Engine
v0.2.0	2025-01-16	Erste Version mit Unterstützung für Basisvalidierung und Dry-Run-Abfragen

Entwicklung und Mitwirkung

Anweisungen zur lokalen Entwicklungseinrichtung und zu den Richtlinien für Mitwirkende finden Sie im CONTRIBUTING.md-Leitfaden.

# Clone the repository
git clone https://github.com/caron14/mcp-bigquery.git
cd mcp-bigquery

# Install development dependencies
pip install -e ".[dev]"

# Execute the test suite
pytest tests/

Lizenz

Dieses Projekt ist unter der MIT-Lizenz lizenziert. Weitere Informationen finden Sie in der LICENSE-Datei.

Available Tools

9 tools

bq_describe_tableB

Get table schema, metadata, and statistics

ParametersJSON Schema

Name	Required	Description
`table_id`	Yes	The table ID
`dataset_id`	Yes	The dataset ID
`project_id`	No	GCP project ID (uses default if not provided)
`format_output`	No	Whether to format schema as table string

TDQS

B3.3/5.0

Behavior3/5

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

With no annotations, the description carries the full burden. It correctly implies a read-only operation ('Get') and lists output categories (schema, metadata, statistics), but does not disclose permissions, latency, or data freshness. Adequate but not detailed.

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 sentence that front-loads the verb and resource. Every word contributes meaning with zero waste.

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?

For a simple read tool with a well-documented input schema and no output schema, the description gives a high-level summary but lacks specifics on the return format, field details, or how it differs from similar siblings. It meets minimum viability.

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?

All 4 parameters are fully documented in the input schema (100% coverage). The description adds no extra parameter semantics beyond the schema descriptions, so baseline 3 applies.

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?

The description clearly states the verb 'Get' and the resource 'table schema, metadata, and statistics'. However, it does not differentiate from sibling tool 'bq_get_table_info', which likely has overlapping functionality.

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

Usage Guidelines2/5

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

The description provides no guidance on when to use this tool versus alternatives like 'bq_get_table_info' or 'bq_list_tables'. It lacks when-not-to-use or context for selection.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

bq_dry_run_sqlB

Perform a dry-run of a BigQuery SQL query to get cost estimates and metadata

ParametersJSON Schema

Name	Required	Description
`sql`	Yes	The SQL query to dry-run
`params`	No	Optional query parameters (key-value pairs)
`pricePerTiB`	No	Price per TiB for cost estimation (defaults to env var or 5.0)

TDQS

B3.1/5.0

Behavior2/5

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

With no annotations provided, the description bears full responsibility for behavioral disclosure. It only states the tool performs a dry-run and returns cost estimates and metadata, but fails to mention key aspects such as idempotency (safe to call multiple times), required permissions, rate limits, or what exactly 'metadata' includes. The non-destructive nature is implied but not explicit.

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 sentence that efficiently conveys the tool's purpose. It is front-loaded and contains no unnecessary words or information, earning its place.

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?

For a tool with 3 parameters (one nested object) and no output schema, the description is minimal. It outlines the goal but omits details on return values, error handling, and edge cases. While the tool is straightforward, the lack of output schema means the description should provide more context on what the agent can expect from the dry-run results.

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 each parameter described in the input schema. The description itself adds no additional semantic meaning beyond what the schema provides. Baseline is 3 because the schema does the heavy lifting; the description does not compensate for any gaps.

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?

The description clearly states the tool performs a dry-run of a BigQuery SQL query to get cost estimates and metadata. It uses a specific verb (dry-run) and resource (BigQuery SQL query). However, it does not distinguish from the sibling tool 'bq_validate_sql', which may also involve checking queries, reducing clarity on when to use each.

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

Usage Guidelines2/5

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

The description provides no guidance on when to use this tool versus alternatives like bq_validate_sql. There is no mention of prerequisites, context, or situations where this tool is appropriate or not, leaving the agent with no decision support.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

bq_extract_dependenciesC

Extract table and column dependencies from BigQuery SQL

ParametersJSON Schema

Name	Required	Description	Default
`sql`	Yes	The SQL query to analyze
`params`	No	Optional query parameters (key-value pairs)

TDQS

C2.9/5.0

Behavior2/5

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

With no annotations provided, the description must fully disclose behavioral traits. It does not mention whether the tool executes the query, is read-only, requires permissions, or has side effects. 'Extract' implies reading but it is not explicit.

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 concise sentence with no redundant information. Every word is necessary.

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?

The tool extracts dependencies but provides no output schema, leaving the agent uninformed about the return format. Given the complexity of dependencies (tables, columns), the description is too sparse.

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 coverage is 100%, so the schema already describes both parameters. The description adds no additional meaning beyond the schema, so baseline score of 3 is appropriate.

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?

The description uses the specific verb 'Extract' and the resource 'table and column dependencies from BigQuery SQL', making the action clear. It implicitly distinguishes from siblings like bq_validate_query_syntax, but does not explicitly differentiate.

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

Usage Guidelines2/5

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

No guidance on when to use this tool versus alternatives (e.g., bq_validate_sql). The description lacks any context about appropriate use cases or exclusions.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

bq_get_table_infoB

Get comprehensive table information including partitioning and clustering

ParametersJSON Schema

Name	Required	Description
`table_id`	Yes	The table ID
`dataset_id`	Yes	The dataset ID
`project_id`	No	GCP project ID (uses default if not provided)

TDQS

B3.1/5.0

Behavior2/5

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

With no annotations, the description must carry the full burden of behavioral transparency. It mentions the tool is a 'get' operation but does not disclose auth requirements, error handling, or whether it is read-only. The inclusion of partitioning and clustering adds some output context but not behavioral traits.

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 that states the tool's purpose without any wasted words. It is maximally concise while still conveying essential information.

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?

For a simple information-retrieval tool with three parameters and no output schema, the description provides the core purpose and hints at output content (partitioning, clustering). However, it lacks details on error behavior, output format, and differentiation from siblings, making it adequate but incomplete.

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?

Input schema has 100% description coverage, so baseline is 3. The description does not add any extra meaning to the parameters beyond the schema, as it focuses on the output instead.

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?

The description clearly states the tool retrieves comprehensive table information including partitioning and clustering. However, it does not explicitly distinguish itself from the sibling tool bq_describe_table, which likely has overlapping functionality.

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

Usage Guidelines2/5

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

No guidance is provided on when to use this tool versus alternatives like bq_describe_table. The description lacks any usage context or prerequisites.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

bq_list_datasetsB

List all datasets in the BigQuery project

ParametersJSON Schema

Name	Required	Description	Default
`project_id`	No	GCP project ID (uses default if not provided)
`max_results`	No	Maximum number of datasets to return

TDQS

B3.1/5.0

Behavior2/5

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

No annotations provided. The description lacks behavioral details such as pagination, rate limits, or error handling. The claim 'List all datasets' is contradicted by the max_results parameter, which limits results.

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?

Single sentence without fluff, but could be more informative about behavior. It is efficient but slightly under-specified.

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?

No output schema, so description should explain return format. It does not. Lacks context on when to use vs siblings like bq_list_tables. Insufficient for an agent to fully understand the tool's role.

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 descriptions cover 100% of parameters with clear meanings. The description adds no extra value beyond the schema, meeting the baseline but not exceeding it.

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 verb 'List' and the resource 'datasets in the BigQuery project', distinguishing it from siblings like bq_list_tables which list tables instead.

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

Usage Guidelines2/5

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

No guidance on when to use this tool vs alternatives (e.g., bq_list_tables for tables). Missing context on prerequisites or suitable scenarios.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

bq_list_tablesA

List all tables in a BigQuery dataset with metadata

ParametersJSON Schema

Name	Required	Description
`dataset_id`	Yes	The dataset ID
`project_id`	No	GCP project ID (uses default if not provided)
`max_results`	No	Maximum number of tables
`table_type_filter`	No	Filter by table types (TABLE, VIEW, EXTERNAL, MATERIALIZED_VIEW)

TDQS

A3.7/5.0

Behavior3/5

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

No annotations provided, so the description bears full burden. It states the operation is a listing with metadata but omits behavioral details like pagination, rate limits, or whether it requires specific permissions. It is not misleading but leaves gaps.

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?

Single sentence of 8 words, front-loaded with purpose. Every word earns its place; no wasted text.

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?

Tool has no output schema. Description says 'with metadata' but does not specify what metadata fields are returned. For a list operation, this is adequate but not complete. Parameters are well-documented, but return value details are missing.

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?

Input schema has 100% coverage with descriptions for all 4 parameters. The description adds no extra meaning beyond 'with metadata', which hints at return content. Baseline is 3 since schema already provides clarity.

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 'List all tables in a BigQuery dataset with metadata', specifying the verb 'list', resource 'tables', and scope 'in a BigQuery dataset'. It distinguishes itself from siblings like bq_describe_table and bq_get_table_info effectively.

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?

No explicit guidance on when to use this tool vs alternatives (e.g., bq_list_datasets lists datasets, not tables). The description implies usage for listing tables with metadata but doesn't provide when-not or exclusion criteria.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

bq_preview_tableB

Get a preview of table data without running a query job (cost-free)

ParametersJSON Schema

Name	Required	Description
`table_id`	Yes	The table ID
`dataset_id`	Yes	The dataset ID
`project_id`	No	GCP project ID (uses default if not provided)
`max_results`	No	Maximum number of rows to preview (default: 5, hard limit: 10)

TDQS

B3.4/5.0

Behavior2/5

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

No annotations are present, so the description must fully disclose behavior. It mentions cost-free and no query job, but fails to describe return format (e.g., rows), ordering (first rows?), or the hard limit of 10 from the schema. This omission leaves significant behavioral 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?

A single, front-loaded sentence with zero wasted words. It efficiently conveys purpose and key differentiator.

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, the description should explain return values (e.g., 'returns first N rows as JSON') but does not. It also omits details like permission requirements or that preview uses the first rows. The tool is simple, but the description is incomplete for safe invocation.

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 coverage is 100% with all parameters described in the schema. The description adds no extra meaning beyond summarizing the schema. Thus baseline 3 is appropriate.

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 action ('preview table data') and key benefit ('cost-free, without running a query job'). It distinguishes from siblings like bq_describe_table (schema) and bq_run_query (costly query). This provides a specific verb+resource with differentiation.

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 use for quick, cost-free previews but lacks explicit when-not or alternative tools. With siblings including bq_get_table_info and bq_dry_run_sql, no comparison or exclusion is provided, leaving guidance implicit.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

bq_validate_query_syntaxC

Enhanced syntax validation with detailed error reporting

ParametersJSON Schema

Name	Required	Description	Default
`sql`	Yes	The SQL query to validate
`params`	No	Optional query parameters (key-value pairs)

TDQS

C2.5/5.0

Behavior2/5

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

No annotations provided, so description carries full burden. It claims 'detailed error reporting' but does not explain what constitutes enhanced behavior, side effects, or limitations. Lacks any behavioral context beyond a vague promise.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is very short (one phrase), but it is front-loaded with functionality. However, it sacrifices necessary detail for brevity, making it less helpful.

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?

For a tool with no output schema and two parameters, the description should explain what 'enhanced' validation entails, expected error messages, or usage examples. It fails to provide sufficient context for effective use.

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 coverage is 100% with adequate parameter descriptions. The tool description adds no additional parameter-specific meaning, achieving only baseline value.

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

Purpose3/5

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

The description states 'syntax validation' which implies validating SQL syntax, but does not explicitly mention SQL queries. The phrase 'enhanced...with detailed error reporting' adds some clarity but fails to distinguish from sibling tool 'bq_validate_sql'.

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

Usage Guidelines2/5

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

No guidance on when to use this tool versus the similar sibling 'bq_validate_sql'. The description offers no context for selection.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

bq_validate_sqlA

Validate BigQuery SQL syntax without executing the query

ParametersJSON Schema

Name	Required	Description	Default
`sql`	Yes	The SQL query to validate
`params`	No	Optional query parameters (key-value pairs)

TDQS

A3.7/5.0

Behavior2/5

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

No annotations are provided, and the description only states the tool validates without executing. It does not disclose what happens on invalid syntax, auth requirements, or whether it checks table existence, leaving significant behavioral gaps.

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 8-word sentence with no extraneous information, achieving maximum conciseness.

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?

Given the tool's simplicity (2 parameters, no output schema, no annotations), the description is minimally adequate but lacks details about return values or behavior on errors, which would help agents understand the output.

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%; both parameters have clear descriptions in the schema. The tool description adds no extra parameter information beyond what the schema already provides.

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 specifies the verb 'validate' and the resource 'BigQuery SQL syntax', and clearly states it does not execute the query, distinguishing it from bq_dry_run_sql which executes a dry run.

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 implies use for syntax checking only, not execution. It contrasts with 'without executing', but does not explicitly name when not to use or mention bq_dry_run_sql as an alternative.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections.

1 tool updatev0.7.0Jun 21, 2026
- Addedbq_preview_table
4 tool updatesv0.2.1May 1, 2026
- Removedbq_analyze_query_performance
- Removedbq_analyze_query_structure
- Changedbq_list_tables1 field changed
  - changedInput schema / properties / max_results / description
```
Previous value: -"Maximum number of tables to return"New value: +"Maximum number of tables"
```
- Removedbq_query_info_schema
9 tool updatesv1.0.0Jan 3, 2026
- Addedbq_analyze_query_performance
- Addedbq_analyze_query_structure
- Addedbq_describe_table
- Addedbq_extract_dependencies
- Addedbq_get_table_info
- Addedbq_list_datasets
- Addedbq_list_tables
- Addedbq_query_info_schema
- Addedbq_validate_query_syntax
2 tool updatesAug 16, 2025
- First observedbq_dry_run_sql
- First observedbq_validate_sql

Deploy Server

Shareable, User-Owned Context BackendPeople, applications, and agents read, write, and coordinate together in one context lake. Reach it over MCP, a CLI, a Python SDK, or REST.fulcradynamics.comAd

TDQS

B3.1/5.0

Scored 2026-09-10 01:01 across 9 tools

Disambiguation2/5

Several tools have overlapping purposes: bq_validate_sql and bq_validate_query_syntax both validate syntax, while bq_describe_table and bq_get_table_info both return table metadata. Agents could easily confuse these and pick the wrong tool.

Naming Consistency4/5

Naming is mostly consistent with a clear bq_ prefix and snake_case verb_noun pattern. The main inconsistency is validate_sql versus validate_query_syntax, which are different names for the same kind of operation.

Tool Count4/5

Nine tools is a reasonable count for a BigQuery exploration and validation server, but at least two pairs are redundant. Trimming the duplicates would make the set tighter without losing any real capability.

Completeness4/5

The server covers dataset/table discovery, schemas, partitioning/clustering metadata, data previews, SQL syntax validation, dry-run cost estimation, and dependency extraction. Minor gaps exist, such as no way to list columns independently or execute queries, but these are outside the validator scope.

Maintenance

ActivitySlowing

ResponsivenessNo issues

Resources

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